Introducing Microsoft Power BI

Introducing Microsoft Power BI
Introducing
Microsoft
Power BI
Alberto Ferrari and Marco Russo
PUBLISHED BY
Microsoft Press
A division of Microsoft Corporation
One Microsoft Way
Redmond, Washington 98052-6399
Copyright © 2016 by Microsoft Corporation
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ISBN: 978-1-5093-0228-4
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Contents
Introduction .................................................... viii
Downloads ..................................................................... xi
Installing the companion content ..................xii
Acknowledgments .....................................................xii
Free ebooks from Microsoft Press .................... xiv
Errata, updates, & book support ....................... xiv
We want to hear from you .....................................xv
Stay in touch ................................................................xv
Chapter 1: Introducing Power BI ..................... 1
Getting started with Power BI ................................ 4
Uploading data to Power BI ................................. 10
Introducing natural-language queries ............. 13
Introducing Quick Insights .................................... 16
Introduction to reports ........................................... 22
Introducing Visual Interactions ........................... 30
Decorating the report ............................................. 37
Saving the report ...................................................... 40
Pinning a report......................................................... 41
iii
Foreword
Refreshing the budget workbook ...................... 43
Filtering a report ....................................................... 50
Conclusions ................................................................. 55
Chapter 2: Sharing the dashboard .................57
Inviting a user to see a dashboard .................... 58
Inviting users outside your organization .... 66
Creating a group workspace in Power BI ........ 71
Turning on sharing with Microsoft OneDrive
for Business ................................................................. 76
Viewing reports and dashboards on mobile
devices........................................................................... 94
Conclusions .............................................................. 101
Chapter 3: Understanding data refresh ..... 103
Introducing data refresh ..................................... 105
Introducing the Power BI refresh
architecture .............................................................. 107
Introducing Power BI Desktop.......................... 111
Publishing to Power BI......................................... 117
Installing the Power BI Personal Gateway .... 120
Configuring automatic refresh ......................... 128
Conclusions .............................................................. 130
Chapter 4: Using Power BI Desktop ........... 132
iv
Contents
Connecting to a database .................................. 134
Loading from multiple sources ........................ 141
Using Query Editor ................................................ 145
Hiding or removing tables ................................. 159
Handling seasonality and sorting months ... 163
Conclusions .............................................................. 179
Chapter 5: Getting data from services
and content packs ........................................ 181
Consuming a service content pack ................. 183
Creating a custom dataset from a service ... 197
Creating a content pack for your
organization ............................................................. 211
Consuming an organizational content
pack ............................................................................. 216
Updating an organizational content pack ... 223
Conclusions .............................................................. 227
Chapter 6: Building a data model ............... 230
Loading individual tables.................................... 232
Implementing measures ..................................... 236
Creating calculated columns ............................. 239
Improving the report by using measures..... 242
Integrating budget information ....................... 244
v
Contents
Reallocating the budget...................................... 256
Conclusions .............................................................. 262
Chapter 7: Improving Power BI reports ..... 264
Choosing the right visualizations .................... 267
Choosing between standard visuals .......... 274
Using custom visualizations .............................. 283
First steps with custom visualizations ....... 284
Improving reports by using custom
visualizations ....................................................... 291
Identifying conditions when custom
visualizations are required ............................. 299
Using DAX in data models ................................. 303
Creating high-density reports .......................... 311
Conclusions .............................................................. 320
Chapter 8: Using Microsoft Power BI
in your company ........................................... 323
Getting data from existing systems ................ 325
Understanding differences between
data refresh and live connections .............. 328
Using relational databases on-premises.. 330
Using relational databases in the cloud ... 335
Using live connections to Analysis
Services ................................................................. 338
vi
Contents
Integrating Power BI with Office...................... 340
Publish Excel data models in Power BI ..... 340
Consume Power BI content from Excel .... 343
Using Power BI Tiles from Office Store .... 350
Managing security to access data ................... 360
Using row-level security ................................. 364
Extending and customizing Power BI ............ 370
Creating custom visualizations for
Power BI ................................................................ 371
Introducing the Power BI REST API ............ 372
Pushing real-time data to Power BI
dashboards .......................................................... 376
Power BI embedded in applications .......... 381
Conclusions .............................................................. 383
About the authors ........................................... 386
vii
Contents
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Introduction
Microsoft introduced the idea of Self-Service
Business Intelligence (BI) back in 2009,
announcing Power Pivot for Microsoft Excel
2010. Strangely, at that time, it did not make big
announcements, hold conferences, or undertake
a big marketing campaign for it. Everything
started slowly, with some enthusiastic users
adopting the new technology, but the vast
majority of people did not even know about its
existence. As part of the community of BI
professionals, we were very surprised by that
approach. At the time, we could clearly see the
advantages for users to begin adopting Power
Pivot as a tool for gathering insights from data,
so this complete lack of marketing was
somewhat disappointing.
Thus, for several years we (as a community) kept
asking Microsoft what they were waiting for;
what was the delay in promoting Self-Service BI
to the greater audience of data analysts, data
scientists, decision makers, and BI enthusiasts all
over the planet. We asked for the ability to share
reports with a team, and the answer was to use
SharePoint, either on-premises or the online
version, with the first release of Power BI—an
viii
Introduction
experience that was still not completely
satisfactory. While we were waiting for Microsoft
to fix the issues with the previous versions and to
begin advertising the current products, it was
doing something different that, with the benefit
of hindsight, looks to have been the perfect
choice. Microsoft collected the feedback of
users, carefully considered what was missing in
the world of end-user BI, and then crafted the
version of Power BI that’s available to you today.
Power BI is an evolution of the add-ins
previously available in Excel: Power Pivot, Power
Query, and Power View. You can use Power BI
with or without Excel—you no longer are
dependent on the version of Microsoft Office
installed at your company. People did not like to
share reports by using only SharePoint, and
Microsoft moved away from it. Users wanted a
mobile experience, and the development team
created it. Data analysts wanted power,
simplicity, new visualizations, and all of this is
now available in Power BI. In addition, a lot of
effort went into the creation of a seamless
experience in loading data from many different
cloud sources and building the infrastructure
needed to provide all BI enthusiasts with a
framework with which they can grow their
reports, share them with their teams, and refresh
the data in a simple yet effective way.
ix
Introduction
To make a long story short, Microsoft heard the
feedback of users and built a great set of tools
for the adoption of Self-Service BI. And, now—
only now—it has begun marketing it.
Suddenly, in the last few months, Power BI has
become the hottest topic at conferences,
webinars, talks, and courses. As expected, people
like you gathered interest in Power BI and began
to search for resources to learn it. This book is
one of these resources and its goal is to provide
you with an effective introduction to the features
available in the new Power BI.
We wanted to write an introduction to Power BI
that covers the basics of the tool and, at the
same time, shows you what the main capabilities
of Power BI are. Thus, it is fair to say that the
content of the book is somewhat unbalanced. At
the beginning, we go for an easy introduction of
the concepts along with an educational
approach that lets you follow on your PC the
same steps we show in the book. However, if we
continued with that same mindset for the entire
book, its size would quickly become intimidating.
Thus, after the first chapters, we begin to run a
bit faster, knowing that we are no longer guiding
you step by step. Instead, we show you available
features; if you want to learn the details, you will
need to read and study more.
x
Introduction
This book is targeted to a variety of readers.
There are information workers and people who
are totally new to the BI world. For those readers,
the book acts as a simple introduction to the
concepts that are the foundation of BI. Yet,
another category of we wanted to target is that
of IT professionals and database administrators
who might need to drive the decisions of the
company in adopting Power BI, because their
users are asking for it. If this is you, this book
acts as both a simple introduction to the basic
concepts, to help you understand why users are
so interested in Power BI, and as an overview of
the capabilities and tools available in Power BI,
so that you can make educated choices in
adopting it. Power BI is not just a tool: it is an
ecosystem that can integrate existing corporate
BI with Self-Service BI. The last chapter of the
book gives you an overview of these capabilities.
We hope you enjoy reading the book as much as
we enjoyed writing it. Keep in mind that this is
probably your first step in the fascinating world
of Self-Service BI, the first step of a long journey
in gathering insights from your data.
Downloads
All of the chapters in this book include
workbooks and databases that let you
xi
Introduction
interactively try out new material learned in the
main text. All sample content can be
downloaded from the following page:
http://aka.ms/IntroPowerBI/downloads
Follow the instructions to download the
IntroPowerBI_302284_CompanionContent.zip
file.
Installing the companion content
Follow these steps to install the companion
content on your computer so that you can use
them with the exercises in this book.
1. Unzip the
IntroPowerBI_302284_CompanionContent.zi
p file that you downloaded from the book’s
website (name a specific directory along with
directions to create it, if necessary).
2. If prompted, review the displayed end user
license agreement. If you accept the terms,
select the accept option, and then click Next.
Acknowledgments
As usual with projects of this sort, there are too
many people to thank, and a complete list of
xii
Introduction
everyone who contributed to this book would be
impossible to write.
Nevertheless, there are certain people we must
mention personally, because of their particular
contributions.
We want to thank Microsoft Press and all the
people there who worked on the project.
Rosemary Caperton has been a great editor who
helped us immeasurably with the process of
book writing. Many others behind the scenes
guided us through the complexity of authoring a
book—thanks to you all.
Finally, a special mention goes to our technical
reviewer, Ed Price. He double-checked all the
content of our original text, searching for errors
and sentences that were not clear; giving us
invaluable suggestions on how to improve the
book. Without his meticulous work, the book
would have been much harder to read and
would contain more mistakes!
If the book contains fewer errors than our
original manuscript, it is only because of them. If
it still contains errors, it is our fault, of course.
xiii
Introduction
Free ebooks from
Microsoft Press
From technical overviews to in-depth
information on special topics, the free ebooks
from Microsoft Press cover a wide range of
topics. These ebooks are available in PDF, EPUB,
and Mobi for Kindle formats, ready for you to
download at:
http://aka.ms/mspressfree
Check back often to see what is new!
Errata, updates, & book
support
We’ve made every effort to ensure the accuracy
of this book and its companion content. You
can access updates to this book—in the form of
a list of submitted errata and their related
corrections—at:
http://aka.ms/IntroPowerBI/errata
If you discover an error that is not already listed,
please submit it to us at the same page.
xiv
Introduction
If you need additional support, email Microsoft
Press Book Support at mspinput@microsoft.com.
Please note that product support for Microsoft
software and hardware is not offered through
the previous addresses. For help with Microsoft
software or hardware, go to
http://support.microsoft.com.
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At Microsoft Press, your satisfaction is our top
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http://aka.ms/tellpress
The survey is short, and we read every one of
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Twitter: http://twitter.com/MicrosoftPress.
xv
Introduction
CHAPTER
1
Introducing
Power BI
David is the manager of budgeting
at Contoso, a company that sells
electronic products worldwide
through several retail shops and a
website. Around the globe,
country/region managers are
responsible for producing figures
for next year’s budget for their
respective countries/regions,
which David then aggregates to
produce the big picture to show to
his boss.
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C H A P T E R 1 | Introducing Power BI
Our scenario begins in October
2015, when David commences
working on the budget for 2016.
As always, David has a Microsoft
Excel workbook containing the
relevant information to produce the
budget. Based on the results of
the workbook, he would typically
create a Microsoft PowerPoint
presentation to share the results
during internal meetings. This
year, however, David wants to
take advantage of the new Power
BI service provided by Microsoft.
This entire book is a journey that we’ll take along
with David as he discovers how Power BI can
help to build a rather sophisticated reporting
solution; in this case, based on a budgeting
2
C H A P T E R 1 | Introducing Power BI
system. But because this book is about Power BI,
not budgeting, we will not focus on the
complexity of building a budget. Instead, we will
keep the budgeting considerations fairly basic,
focusing on the complexity of teamwork, data
modeling, and reporting.
We provided all the workbooks and databases
that we used to build the demonstrations in the
companion content for this book. If you are
interested in learning the basics of Power BI, you
can replicate David’s activities on your computer
so that you can augment your learning
experience by following the examples. Be aware,
though, that the results you obtain by running
the demonstrations might be slightly different,
and the appearance of webpages and the user
interface might not be identical, either. Power BI
is evolving very quickly, and we tried our best to
show examples that will last some time.
Nevertheless, differences might occur; thus, you
should concentrate on learning the features of
Power BI, not the demonstrations. So, even if the
numbers end up being different, what’s
important is to absorb how to do something, not
just replicate what you read in the book.
Moreover, we strongly encourage you to test
Power BI using your own data. You can perform
the same operations on your personal
3
C H A P T E R 1 | Introducing Power BI
information that we describe in the book, thus
reaping the combined benefits of learning the
Power BI tools while simultaneously gaining
insights into your data.
Getting started with
Power BI
Any journey begins with the first step, so let’s
take that step together.
David obtained from IT an Excel report that
contains the sales for the past three years,
divided by country/region, brand, and month.
Sales in Contoso are strongly brand-oriented,
and some brands are prone to seasonal effects
that David wants to take into account. For this
reason, he uses data grouped by month. Figure
1-1 shows a small portion of the resulting data,
which he stores in an Excel file. If you would like
to become more familiar with David’s data, you
can open 2015 Sales.xlsx from the book’s
companion content.
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C H A P T E R 1 | Introducing Power BI
Figure 1-1: An excerpt from the initial Excel workbook
for David’s budget plan.
Every year, David makes some considerations on
these numbers and then he shares his findings
with the country/region managers, who then
send back to him workbooks with their numbers
for the next year. Figure 1-1 shows some data
from China, but there are several other
countries/regions, as well. During the process
of computing those numbers, there are many
meetings and discussions in which the managers
bring their experience and knowledge to bear on
the process, adding their own versions of the
original workbooks, each displaying various
charts and calculations, which must all be
explained to others. This is a daunting task, to
be certain, and one that David would like to
streamline.
5
C H A P T E R 1 | Introducing Power BI
Fortunately, David heard about an interesting
tool called Power BI that Microsoft created in
2015 that might be helpful toward creating a
collaborative environment in which any
stakeholder of the budgeting process can share
his findings with others, working together on the
goal. But, at this point, the name and maybe a
marketing video is all that David knows about
Power BI.
Driven by curiosity, he navigates to
www.powerbi.com and starts down his learning
path. Figure 1-2 depicts the welcome page of
the Power BI website.
Figure 1-2: The welcome page of Power BI, the
starting point of David’s journey.
To begin, David clicks the Get Started Free
button. He is then offered a choice as to which
6
C H A P T E R 1 | Introducing Power BI
experience he would prefer to use: he can
choose either Power BI Desktop For Windows or
just Power BI, as shown in Figure 1-3.
Figure 1-3: You can start with Power BI by using
either of the two main experiences.
Actually, there is very little difference between
the two. In fact, Power BI Desktop and Power BI
are two sides of the same coin: Power BI Desktop
is a Windows application running on your PC,
whereas Power BI is a cloud service that you use
through the web browser. In both cases, you will
be able to perform the same operations, albeit
with some subtle differences. Moreover, the two
tools complement each other, and you are likely
to use both to build your dashboards.
7
C H A P T E R 1 | Introducing Power BI
After reading the descriptions, David correctly
concludes that Power BI Desktop is designed for
more advanced tasks. Given that he’s just
beginning to learn about it, he opts for plain,
vanilla Power BI.
When David clicks the Sign Up button, the
screen shown in Figure 1-4 appears. Power BI is a
web service to which you can upload data and
build insightful dashboards and charts. As with
any web service, you need to sign in, but Power
BI does not require much in the way of
credentials: to get started, all you need is a
valid email address, which David provides.
Figure 1-4: You need only a valid email address to
gain access to Power BI.
After clicking Sign Up, Power BI informs David
that he already has a subscription to Microsoft
8
C H A P T E R 1 | Introducing Power BI
Office 365; those credentials are sufficient to
gain access to Power BI.
Note If you do not have an Office 365 account,
Power BI will send you an email with a link to
complete the registration process. (Be aware
that you cannot use a personal email service
such as Hotmail, Yahoo, or Gmail.) This is to
ensure that you actually own the email address.
Following the link directs you to the
registration page, where you provide some
basic details such as first name, last name, and
so on. In both cases, no credit card or any other
form of payment is required, because most of
the features of Power BI are totally free.
On the same No Need To Sign Up page, David
could click OK, Got It to sign in without any
additional steps. Rather than do that, he goes
back to the sign-in page, but instead of clicking
Sign Up, he signs in to the portal by using the
Sign In button (see Figure 1-2) and then provides
his Office 365 credentials. The system takes a few
seconds to prepare his account, after which
David gets his first glimpse at the Power BI
portal, as shown in Figure 1-5.
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C H A P T E R 1 | Introducing Power BI
Figure 1-5: The introduction page of the Power BI
portal.
Uploading data to
Power BI
David has an Excel workbook that he wants to
upload to Power BI to see what it has to offer.
Because the data is stored in a local file on his
laptop, he clicks the Get button on the Files tile
(see Figure 1-5). This displays the screen in
Figure 1-6, where he can then choose from
among several upload options.
10
C H A P T E R 1 | Introducing Power BI
Figure 1-6: Some of the file uploading options in
Power BI.
We will explore these options at greater length
in the chapters that follow. For now, David
chooses Local File, navigates to a file on his
laptop named 2015 Sales.xlsx, and then clicks
Open to upload the workbook to Power BI. After
a few seconds, the Power BI dashboard displays
the screen depicted in Figure 1-7.
Figure 1-7: This is how the Power BI service looks
after you load an Excel workbook.
11
C H A P T E R 1 | Introducing Power BI
Before going any further, we want to take a few
moments to explain how the Power BI portal is
organized. On the left side of the screen, in the
pane labeled My Workspace, there are several
items. Let’s take a look at them:



Dashboards This lists all of the dashboards
you have created. After loading a single
workbook, Power BI creates a dashboard for
you, using the same name as that of the
original workbook.
Reports Here, you will see the reports
based on your data. In Figure 1-7, there is no
default report, but we’ll follow along as
David creates one very soon.
Datasets This lists all of the data sources
that you connected to Power BI. In our
narrative thus far, the only workbook David
loaded is 2015 Sales.
The Power BI experience is all about gaining
insights from data. You begin with a dataset
(2015 Sales, in this example), you then build
reports on the data, and, finally, you organize
visualizations of the reports into dashboards.
You will learn how to perform all of these
operations in detail in this book. For the
moment, we want only for you to become
acquainted with the basic operations.
12
C H A P T E R 1 | Introducing Power BI
Referring back to Figure 1-7, the central pane is
positioned on the 2015 Sales dashboard and,
because David has loaded the file but has not
yet performed any analysis on the data it
contains, the dashboard is essentially empty,
showing only the Ask A Question box and the
2015 Sales.xlsx tile, which indicates that the
dashboard is indeed connected to his Excel
workbook.
Introducing naturallanguage queries
With Power BI, you have the ability to carry out
analysis of your data by asking it questions, in
plain English—no special code or syntax is
required. This feature is called natural-language
queries, and with it, you can ask Power BI to
perform tasks in much the same way you would
ask one of your colleagues. Let’s take a look at
an example of how David uses natural-language
queries in Power BI.
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C H A P T E R 1 | Introducing Power BI
In the central pane, in the question box, David
types a simple query: “Show sales 2015 by
brand.” Power BI understands the query and
presents a bar chart (see Figure 1-8) in which the
brands are displayed alphabetically and the
length of the bars is proportional to the
corresponding sales for each brand in 2015.
Figure 1-8: Power BI understands queries in natural
language and displays the data you request.
Not only did Power BI understand David’s query,
but, after performing an analysis of his dataset, it
also suggests other meaningful queries in a list
that appeared when he began to type the query.
For David’s data, that analysis revealed that he
might also be interested in viewing sales in 2015
by country/region or by month, so Power BI
suggests those as alternate queries.
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C H A P T E R 1 | Introducing Power BI
Also in Figure 1-8, notice the highlighted
pushpin icon to the right of the question box.
You can click this to “pin” the currently displayed
visualization to the dashboard; this way, you can
easily see it when you connect to Power BI.
When you click the pushpin button, Power BI
opens the Pin To Dashboard dialog box shown in
Figure 1-9.
Figure 1-9: Using the Pin To Dashboard dialog box,
you can choose to pin a visualization to an existing or
a new dashboard.
To save the newly created bar chart to the
dashboard, click Pin. Figure 1-10 shows how
Power BI presents the dashboard with the
pinned bar chart. (You need to go back to the
dashboard to see it.)
15
C H A P T E R 1 | Introducing Power BI
Figure 1-10: The dashboard is a container for
visualizations created on top of datasets.
Using natural-language queries is quite
impressive, but it is only one of the many ways in
which Power BI can analyze your data.
Introducing Quick
Insights
Another feature that is worth learning as soon as
you begin using Power BI is Quick Insights. With
this feature, Power BI can search a dataset for
interesting patterns and provide you with a list
of charts that help you to better understand your
data.
To activate Quick Insights, click the ellipsis to the
right of the dataset (see Figure 1-11) on which
you want to perform the analysis: in David’s case,
16
C H A P T E R 1 | Introducing Power BI
that’s “2015 Sales”. This opens the dataset menu;
here you choose Quick Insights. When David
clicks Quick Insights, the button changes to View
Insights.
Figure 1-11: You can activate Quick Insights from the
dataset menu by clicking Quick Insights.
The first time you run Quick Insights on a
dataset, Power BI schedules an analysis of that
dataset. This might last for some seconds or
minutes, depending on the size of the data.
When the search for insights is complete, Power
BI notifies you. Of course, whenever you update
your dataset, this search operation will need to
be repeated. However, as long as the dataset
remains unchanged, the insights will be
immediately available.
17
C H A P T E R 1 | Introducing Power BI
But, what are these insights?
The basic idea is that Power BI can use artificial
intelligence to analyze your data, searching for
some useful or interesting patterns. It uses very
sophisticated algorithms whose speed depends
on the size and complexity of the dataset.
Obviously, on a small dataset such as the one
David uploaded, finding insights takes no more
than a few seconds. As soon as the search is
complete, you can access it. On the Insights Are
Ready dialog box, David clicks View Insights.
Figure 1-12 presents the first two insights that
Power BI found on David’s file. Many others are
within the list, so many, in fact, that they would
not fit on the page in this book. David scrolls
down to view them all.
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C H A P T E R 1 | Introducing Power BI
Figure 1-12: Quick Insights are a powerful analytical
tool to glean information from your data.
The first insight shows that the United States
accounts for most of the sales of the A. Datum
brand, compared with China and Germany. The
second insight reveals a substantial seasonaleffect increase in sales for the month of March
for Adventure Works and Contoso. If you run
Quick Insights on the data, you will likely get
different insights, which Power BI chooses to
display at the top.
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C H A P T E R 1 | Introducing Power BI
Of course, insights are gathered by Power BI
without it having any knowledge of your
business or the economic scenario as a whole, so
there might be many different reasons that
explain the data and findings. Power BI cannot
replace your brain when it comes to interpreting
the numbers, but it can prove extremely useful
because it can easily find some points of
interests in your data by using the brute force of
algorithms.
The best way you can use Quick Insights is to
browse through them, looking for the
confirmation of what you already know about
your data and, at the same time, for fresh ideas.
It might be the case that some of the insights are
not really meaningful, but, with the sheer
number of insights that Power BI finds for you,
it’s likely that there are some real hidden gems
that might improve your knowledge of your
numbers.
More info You can find a more complete
description of the algorithms used by Power BI
and the types of insights that it can reveal by
going to https://powerbi.microsoft.com/enUS/documentation/powerbi-service-autoinsights-types/. Of course, with newer versions
of the analytics engine, the numbers and the
quality of insights might change and improve.
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You can click any insight to enlarge it. If you
hover over one, the same pushpin button as that
of the natural-language query appears so that
you can pin the insight to the dashboard if you
want. David clicks the Category Outliers Insight
from Figure 1-12 to enlarge it, clicks the Pin icon,
and then in the Pin To Dashboard dialog box, he
leaves Existing Dashboard selected and clicks Pin
to pin it to his dashboard.
Pinning one of the insights to the dashboard
makes it more interesting. Moreover, by doing
that, you will learn that you can move and resize
visualizations pinned to a dashboard by using a
convenient grid, making them more aesthetically
appealing. David returns to look at his expanded
2015 Sales.xlsx dashboard, and he moves the
new Sale 2013 By Brand visualization below the
others. Figure 1-13 demonstrates David’s
dashboard, which now contains two
visualizations.
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C H A P T E R 1 | Introducing Power BI
Figure 1-13: A dashboard can contain multiple
visualizations organized in a grid, individually moved
and resized.
Introduction to reports
So far, David has used only automated report
building, using a natural-language query as well
as the Quick Insights feature. As you might
imagine, he only scratched the surface of Power
BI’s reporting capabilities. In fact, he can build
reports manually, unleashing the full potential of
Power BI visualizations.
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To create a new report, in the Datasets section of
the navigation pane, click a dataset. David clicks
2015 Sales. Power BI opens an empty report
based on that dataset, as illustrated in Figure 114.
Figure 1-14: Clicking a dataset creates an empty
report based on that dataset.
The user interface of a report is very powerful
because it combines many different features in a
single window. On the far left is the standard
Power BI navigation pane. The central pane is the
canvas on which you can build a report by
adding visualizations. Here, you can also
configure the properties of each visualization. On
the right are two panes: Visualizations and Fields.
The Visualizations pane offers the entire set of
available visualizations at the top; the bottom
section presents filtering options. The Fields
pane contains the list of all the fields of your
dataset. In David’s case, you can see how the
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C H A P T E R 1 | Introducing Power BI
Fields pane lists the columns of the Excel table
he uploaded to Power BI.
Figure 1-15 shows an enlarged view of the Fields
pane. If you focus your attention on the
individual columns there, you can see that some
of them have a small icon beside their names.
This icon identifies the main usage of the field.
For example, the fields Sale 2013, Sale 2014, and
Sale 2015 each have a summarization icon (a
Greek sigma), indicating that the total for each
column will be displayed if used in a report. The
CountryRegion field shows a small globe,
indicating that this field contains geographical
data, and it will be used to draw data on maps.
Figure 1-15: Many columns in the Fields list display a
small icon. The icon indicates the default aggregation
it uses.
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C H A P T E R 1 | Introducing Power BI
To create a report, select the fields that you want
to appear in the report. For example, referring
back to Figure 1-14, in the Fields pane, David
clicks Brand and then Sale 2015. Because Brand
has no summarization icon, it is used to slice
data, whereas Sale 2015, which displays a sigma,
will present the sum for that column, generating
the report shown in Figure 1-16.
Figure 1-16: A first visualization based on Brand and
Sale 2015.
What David just created is the default
visualization; that is, it’s a grid with the brands
and the sum of Sale 2015 on the rows, showing
raw numbers. Numbers are very interesting, but
they do not give a clear idea of the relationship
among them. In fact, at first glance it’s not
evident which brand is the most important one,
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C H A P T E R 1 | Introducing Power BI
which ones are the smallest, and what the
relative importance of the numbers is. Charts, on
the other hand, can give viewers a much quicker
understanding of the data.
You can modify the visualization of a tile by
choosing one of the many available types of
charts in the Visualizations pane. For example,
you can use a column chart by first selecting the
visualization and then clicking the column chart
icon, which is among the many highlighted in
Figure 1-17.
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C H A P T E R 1 | Introducing Power BI
Figure 1-17: The Visualizations pane offers many
different visualizations to use in your reports.
Note If you click a visualization type but do
not have a specific tile selected, Power BI
inserts a new, empty visualization. If this
happens to you, do not worry: just select the
empty chart and delete it by pressing the
Delete key. Then, select the tile that you want
to change and try again.
As Figure 1-18 so clearly demonstrates, the same
numbers—Sale 2015 by Brand—shown in a
column chart are much easier to understand.
Figure 1-18: With the correct visualization, numbers
are much more meaningful.
Note Before proceeding further, feel free to
experiment by using different visualizations for
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C H A P T E R 1 | Introducing Power BI
the same data. As you will discover, each
visualization offers a different insight from the
same numbers. With Power BI you can use
different visualizations to find the best way to
tell a story about your data, using the same
numbers.
So far, you’ve learned how to create an
individual chart. But one chart alone is not yet a
full report. If you click an empty area of the
central canvas and repeat the aforementioned
procedure, but, adding the CountryRegion and
Sale 2015 fields, you will generate a new tile,
this time displaying a map with sales in the
three countries/regions contained in the
demonstration dataset, as shown in Figure 1-19.
Figure 1-19: A map of the world showing sales in
different countries/regions, highlighted as bubbles.
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Now, maps are powerful charting tools, but, as
Figure 1-19 demonstrates, by having only three
values they look dispersive. There are too many
details in the map, whereas the goal is to show
only the relative size of three areas. In this case, a
column chart does this job well. You can
transform the map into a column chart and then
move the two visualizations so that they look like
those shown in Figure 1-20.
Figure 1-20: A report can contain multiple
visualizations.
As you have seen, a report is a collection of
visualizations organized in such a way as to
communicate insights about the data. In Figure
1-20, a reader has an immediate feeling that
sales in China, Germany, and the United States
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are nearly the same. Also it is clearly evident that
there are only a few brands that make up most
of the sales (Contoso, Fabrikam, and Litware),
whereas others (Northwind Traders and Tailspin
Toys) produce only a relatively tiny amount of
sales.
Introducing Visual
Interactions
This feature is very similar to what David could
have achieved by using Excel and a couple of
pivot tables on top of the table containing sales,
yet there are some important differences
between a report created in Excel and the same
report done by using Power BI. We will look at
those as you proceed through the book, but, for
the moment, let’s look at the interactive nature
of Power BI reports.
In the top chart from Figure 1-20, click the
column for Germany. As soon as you click an
element within the chart, the entire report is
filtered showing the contribution of Germany to
sales of different brands, by means of coloring
with two shades the Sale 2015 By Brand
visualization, as depicted in Figure 1-21.
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C H A P T E R 1 | Introducing Power BI
Figure 1-21: Clicking on one column in the column
chart filters the bar chart, highlighting the contribution
against the total.
By doing this simple operation, David notes that
sales of Northwind Traders in Germany are tiny
when compared with China and the United
States. Clearly, that brand is not popular in
Germany, and David is curious to see whether it
is sold in equal volumes in China and United
States or whether one of those countries/regions
has much more sales than the other one.
To perform this analysis, he clicks the bar for
Northwind Traders. By doing so, the filter will
move from the country/region to the brand and,
as it happened before, the country/region chart
will highlight the contribution of Northwind
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C H A P T E R 1 | Introducing Power BI
Traders to the total sales, as shown in
Figure 1-22.
Figure 1-22: Filtering one brand shows the
contribution of the brand against the total of sales by
country/region.
The chart with sales by country/region already
shows that a majority of sales are in United
States, but because David is analyzing a very
small brand, the chart is not clear in terms of
relative importance of sales in different
countries/regions.
Before we move on, we now need to be a bit
more accurate in describing what we are seeing.
Any chart produces graphical visualizations of
the underlying numbers. Any of those
visualizations can behave as a filter, and such a
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filter is activated by simply clicking the chart. So
far, you have seen that a filter—when applied to
other charts—highlights the relative contribution
of the filtered item against the grand total by
using two colors. This behavior is known as
visual interaction, and it is extremely interesting.
Yet, there are scenarios, like the one David is
experimenting with, for which it would be better
to compare the differences between
countries/regions more than the overall
contribution of a brand against the other brands.
You can configure visual interactions in a highly
precise way. Namely, you can configure how the
filtering on a chart behaves with respect to all of
the other ones. The scenario we are looking at—
with only two charts—is perfect for
experimenting because it is very simple. To
configure visual interactions, on the top menu
bar of the report, click the Visual Interactions
button, which you can see highlighted on the
right in Figure 1-23.
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C H A P T E R 1 | Introducing Power BI
Figure 1-23: When you turn on visual interactions,
you can configure how a chart interacts with other
charts.
When you turn on visual interactions, each chart
shows a different set of icons. The one you select
(in this example, Sale 2015 By Brand) shows the
standard selection icon, whereas all of the others
(Sale 2015 By CountryRegion) show the three
different kinds of interactions you can choose:

34
The first is the filtering interaction (the
funnel icon). When you click this, filtering the
selected chart will place the very same filter
on the destination chart. In such a case, you
will not see the contribution of the selection
to the total. Instead, you will see only the
C H A P T E R 1 | Introducing Power BI
selection in the chart, excluding the values
(and corresponding areas) related to
unselected items.
The second interaction is a pie chart (the pie
icon); that is, the relative contribution. This is
the default filtering behavior, where the
filtering on one chart shows, on the
destination chart, the relative contribution of
the selection against the total.

The third is the no filtering interaction. When
this is the selected behavior, filtering the
selected chart has no effect on the target
chart.

Note Visual interactions are much easier to use
than to explain in a book that, by nature,
contains static figures. If you are still not clear
on the behavior of filtering, try it yourself; you
will understand it in a much easier way.
For example, you can select the filtering
interaction from the sales by brand to the sales
by country/region. By doing so, when you select
Northwind Traders, the resulting report will show
a different result, as shown in Figure 1-24.
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C H A P T E R 1 | Introducing Power BI
Figure 1-24: By using the filtering behavior, the
relative size of the bars in the Sale 2015 By
CountryRegion chart is more meaningful.
Now, when you browse the report, you can
quickly click a brand and see which
country/region sold more than the others.
Because of automatic determination of the scale,
the insights are much clearer.
Note You can configure the filtering behavior
for any two pairs of visualizations. To perform
this, you first select the source (that is, the tile
from which you want to filter) and then choose
the proper action on the destination
visualization. Needless to say, you need to pay
attention because mixing different filtering
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behaviors on the same page can result in some
complexity and confusion when using the
report.
Decorating the report
In the previous sections, David performed some
analysis on the data, and now he thinks that his
first report, although simple, contains some
findings that are worth sharing. He can obviously
make a screenshot and attach it to an email with
some description, but Power BI offers some tools
that make it possible for him to annotate a
report with remarks.
David can add text to the report and decorate it
with shapes. For example, he can add a colored
arrow to Northwind Traders and a text box with
some remarks about what he found. When David
does this, the report is easier to read, as
demonstrated in Figure 1-25.
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C H A P T E R 1 | Introducing Power BI
Figure 1-25: Decorating the report makes it easier for
the reader to immediately understand the insights.
To add the text, above the central pane, David
clicks Text Box. When the text box appears, he
types and formats his text. To add the arrow,
again above the central pane, David clicks
Shapes and then Arrow. He then moves the
arrow into position and resizes it.
When you add the text box or the arrow, you
need to set some properties for these objects. In
fact, the appearance of each object (either a
decoration or a full chart) in a Power BI report is
controlled by a set of properties that you can
access by clicking the object in the central pane.
The properties that you can set appear in the
Visualizations pane, as shown in Figure 1-26.
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C H A P T E R 1 | Introducing Power BI
Figure 1-26: Each visualization has a set of
properties that you can adjust to customize it.
For example, to rotate the arrow, David selects it
and then, in the Format Shape pane on the right,
he clicks Rotation and drags the slider. Similarly,
he also changes the fill color.
Finally, keep in mind that visual filters on reports
(that is, filters that you set by clicking a chart
item) are not saved as part of the report. Thus,
when you look at the report again, the arrow is
useful to tell you where to apply the filter to see
the data. Later in this chapter, you will learn how
to place a permanent filter on a report.
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Saving the report
At this point, David can save his report so that he
can continue working on it later. To save a
report, go to File, click Save (see Figure 1-27),
and then provide a name for the report. Here,
David saves his report with the name Northwind
Traders.
Figure 1-27: When you finish editing a report, saving
it is always a good idea.
After you save the report, it appears in the My
Workspace pane, in the Reports section. You can
now access it any time you sign in to Power BI.
When you select a saved report, it opens and
remains in read-only mode until you explicitly
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C H A P T E R 1 | Introducing Power BI
activate it for editing by clicking the Edit Report
button highlighted in Figure 1-28.
Figure 1-28: You need to click the Edit Report button
to bring a saved report in edit mode.
This behavior is useful to avoid unintentional
editing of the report. A saved report can be
easily viewed, and it always reflects the latest
data. If you need further filtering or you want to
perform a different analysis on the same report,
you need to turn on edit mode.
Pinning a report
When you open a report in read-only mode, the
menu bar at the top of the screen offers you
several actions: you can choose to save a copy of
the report under another name, edit or print it,
and apply different visualizations. All of these
operations are seamless and need no further
explanation.
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But, one of the menu bar items is worth a few
moments of our attention: Pin Live Page (see
Figure 1-29).
Figure 1-29: Visualizations aren’t the only things that
you can you can pin to the dashboard: You can pin
reports, too.
What is the difference between pinning a
visualization and pinning a full report? When you
pin a visualization, Power BI saves it as it is, but
the visualization is disconnected from any others
in the same dashboard. Thus, any visualization in
a dashboard does not include the visual
interactions of other visualizations. This is usually
good, because a dashboard is not intended for
interaction. If you need interaction or further
analysis, you can always click a visualization from
the dashboard to open the source report.
Nevertheless, sometimes you want to keep visual
interactions between some components of your
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C H A P T E R 1 | Introducing Power BI
dashboard. If this is your goal, you need to build
the report and pin it in its entirety as a live page.
Visualizations belonging to the same live page
will maintain the behavior of visual interactions,
albeit they will be limited to the visuals in the
report. In other words, visualizations belonging
to the same report can interact among
themselves, whereas filtering them has no effects
on other visualizations in the same dashboard.
For example, after David adds his Northwind
Traders report to his dashboard, the two charts
are mutually interactive, but they don’t affect the
other visualizations in the dashboard, and those
other visualizations don’t affect the two brought
over in the report.
Refreshing the budget
workbook
So far, David has learned the basics of Power BI
and ended up with some useful findings that he
will want to share with the country/region
managers. Nevertheless, before continuing,
David is worried about how he will refresh his
data when new figures become available. In fact,
you might remember that he began building the
budget in October. Thus, new data will be
arriving over time for sales, and the
country/region managers will provide new
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C H A P T E R 1 | Introducing Power BI
forecasts that David will need to add to his
workbook. How will he upload new data to the
service to refresh the existing data?
If fact, there are many ways by which you can
refresh data in Power BI.
David receives figures from the country/region
managers in a very simple form: They each send
him a workbook with forecasts based on the
brand, with no monthly details. Figure 1-30
shows what the forecasts look like for China.
Figure 1-30: An example of forecasts received from
the China manager.
Because the forecasts are at the year level, but
David set them up at the month level, he opts
for a simple solution: divide the yearly sales by
12 and copy the result to his own workbook in a
new column called Budget.
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C H A P T E R 1 | Introducing Power BI
The workbook with the new Budget column now
looks like Figure 1-31. Looking at the numbers, it
is clear that David should have used a different
allocation, because the numbers do not reflect
the seasonal nature of sales and, more
important, they are not correct. We will fix this
and use a better technique later in the book.
Right now let’s focus on Power BI. Remember,
this is not a book about budgeting techniques.
Figure 1-31: The 2015 Sales workbook now contains
monthly sales in the last column, named Budget.
Now, David faces this scenario: the workbook on
his laptop has different numbers and a different
structure (the Budget column is new), whereas
the workbook he uploaded into Power BI still
retains the old values and model.
The simplest way that comes to mind to refresh
the workbook is to upload it again to the Power
BI service. David follows the same upload
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C H A P T E R 1 | Introducing Power BI
procedure he used the first time, but when it is
about to complete the upload, Power BI issues
the warning shown in Figure 1-32.
Figure 1-32: If you upload the same workbook twice,
Power BI issues a warning about possible data loss.
The error message is not totally clear. It states
that David is going to lose changes to reports
online, but he did not make any changes. He
created several reports, without modifying them.
But, David is nothing if not a brave and cavalier
sort, so he clicks Replace It to see what happens.
Note Even if it might be obvious, it is worth
remembering that Power BI is an online service.
When it comes to datasets, you cannot use the
standard technique of “making a copy of the
workbook before replacing it” that you
probably use on your PC.
After uploading the file, the old workbook is
replaced with the new one and all the reports
and the dashboard look identical. David did not
lose anything. In reality, the warning pertains to
Power View reports that might have been
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C H A P T E R 1 | Introducing Power BI
automatically created within the Excel data
model, a feature that you did not learn yet. So,
David breathes a sigh of relief and pushes
onward.
Note Be aware that there might be a delay of
several minutes from when you upload a new
version of a dataset until the new columns and
tables appear in Power BI. The exact timing
depends on whether there is a recent release of
Power BI and is subject to change in the future.
If, for any reason, you do not see updated
information after a new upload, just wait a few
minutes and try it again; Power BI is being
refreshed and nothing is going wrong.
Now, the Power BI model contains the new
Budget column, which David can use to build
more interesting reports. It is worth noting that a
report can contain multiple pages. Thus, he can
add different visualizations to the 2015 Sales
report. For example, he created the report page
depicted in Figure 1-33.
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C H A P T E R 1 | Introducing Power BI
Figure 1-33: With the Budget in the model, reports
are richer and provide better information.
Observe that the two visualizations at the
bottom use a different visual interaction method.
The chart on the left shows the contribution of a
brand to the overall sales and budget, whereas
the one on the right is more useful to perform a
comparison of budget and sales in different
countries/regions. Both are useful and provide
different insights. The technique of adding
multiple copies of the same visualization with
different visual interactions is common, and we
encourage you to learn to use it.
You might have noticed that to make it more
evident, David uses different titles for the two
visualizations (and different font sizes, too). You
can manage these visualization details by using
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C H A P T E R 1 | Introducing Power BI
the brush icon highlighted in Figure 1-34, which
shows the many options to configure a visual. In
the example, we used a custom title and
changed the font size.
Figure 1-34: You can configure many aspects of a
visualization by using the formatting options.
You will learn many more details about
visualization formatting as the book progresses.
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However, for an introductory chapter, this is
enough. It’s time now to draw some conclusions.
Filtering a report
You already learned about the visual interactions
feature, which makes filtering a report a breeze.
Visual interactions are useful, but they come with
some limitations:


The filter is not saved as part of the report.
Whenever you open a report, you can begin
to play with visual filters but there is no way
to store the filter in the saved report.
The filter is always visible. Sometimes you
want a filter for the entire report, but you do
not want any visual indication of the filter
being applied. In other words, you want
something like a hidden filter working in the
background on the full page or report.
Power BI offers you a different way of filtering
data. They are the standard filters (as opposed to
visual filters), and they can be applied to three
different layers:

50
Visual-level filters Visual-level filters work
on only an individual visualization, reducing
the amount of data that the visualization can
C H A P T E R 1 | Introducing Power BI
see. Moreover, visual-level filters can filter
both data and calculations.


Page-level filters Page-level filters work at
the report-page level. Different pages in the
same report can have different page-level
filters.
Report-level filters A report-level filter
works on the entire report, filtering all pages
and visualizations included in the report.
You can set all of the filters in the Filters section
in the Visualizations pane. Figure 1-35 illustrates
that for David’s report, there are three kinds of
filters.
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C H A P T E R 1 | Introducing Power BI
Figure 1-35: You can configure filters in the same
place, in the Filters section of the Visualizations pane.
You can drag columns from the Fields in any
filter and, when there, you can click them to
apply a filter, by simply selecting some values
from the list.
For example, Figure 1-36 shows the result if you
add a page-level filter to the report, selecting
only China and Germany.
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C H A P T E R 1 | Introducing Power BI
Figure 1-36: The same report as the report presented
in Figure 1-33, this time filtered using only China
and Germany.
Filters at the report and page level behave the
same way. Filters on a visualization, on the other
hand, have an additional feature: they can filter
both data (as was the case for the
country/region) or the metric associated with the
chart.
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C H A P T E R 1 | Introducing Power BI
For example, you can filter the upper-right chart
to include only values for which the budget is
greater than 50,000. Figure 1-37 presents the
result.
Figure 1-37: A visual-level filter can filter the measure
used to draw the chart.
Notice in Figure 1-37 that the number of brands
is much less than that of Figure 1-36. This is
because the latter report shows only brands that
have a Sale 2015 measure greater than 50,000.
All of these filters are saved as part of the report,
and they are not shown in any visual way. For
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C H A P T E R 1 | Introducing Power BI
this reason, if you prepare a report that, for
example, filters only 2015, it is always useful to
add a description of the filter as part of the
report title—“Sales in 2015” instead of “Sales.”
Conclusions
After this first tour in Power BI, it’s now time to
take a breath and describe what we’ve learned
so far.




55
Power BI is a cloud service that provides
tools to perform analysis of data and gain
insights from your numbers.
To build a dashboard, you need a dataset, a
report, and, finally, the dashboard. The
dataset is the source of data, reports are
useful to create visualizations that might be
connected through visual interactions, and a
dashboard is a collection of visualizations
and/or reports.
You can create visualizations by using
natural-language queries, Quick Insights, or
full reports.
You can decorate a report by using text
boxes, shapes, and pictures.
C H A P T E R 1 | Introducing Power BI




56
Visualizations in a dashboard are not
connected through visual interactions, which
work only among visualizations in a report. If
needed, you can pin a report as a live page
in a dashboard to maintain the interaction
capability.
You can load data in Power BI from many
different sources. So far, David has used only
an Excel workbook, but there are many other
sources that he will learn to use before
becoming a Power BI expert (and that you
will learn about later in this book).
You can refresh the content of your
workbook by uploading a new version of it.
But, as Chapter 2 describes, there are better
ways to refresh data.
You can apply filters by using visual filters,
which produce highly interactive reports, or
you can use static filters, which you can
apply at the visual level, page level, and
report level. Static filters are saved as part of
the report, whereas visual filters are not.
C H A P T E R 1 | Introducing Power BI
CHAPTER
2
Sharing the
dashboard
In Chapter 1, David, our manager
of budgeting at Contoso, created
his first dashboard with analysis of
sales. But that dashboard is only
the starting point for creating the
budget for 2016. David will involve
his colleagues in this process, so
the first thing he needs to do is
share the work he has done thus
far with these colleagues. After
that, he must collect feedback and
numbers from other managers in
order to complete the entire
budget. Also, he needs to choose
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C H A P T E R 2 | Sharing the dashboard
how to share the resulting data
and reports between members of
the team.
In this chapter, you will see how
David can use the features of
Microsoft Power BI, along with
other services, to achieve his
goals.
Inviting a user to see a
dashboard
David wants to share the dashboard he created
with his colleague Wendy, the country/region
manager for Germany. To do that, he opens the
dashboard in its own Power BI account and then
clicks the Share button located in the
dashboard’s upper-right corner (see Figure 2-1).
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Figure 2-1: To share a dashboard, click the Share
button in its upper-right corner.
This opens the Share Dashboard dialog box in
which David can send Wendy an invitation.
Before we continue along with David, let’s take a
closer look at this dialog box. The Share
Dashboard dialog box has two tabs: Invite and
Shared With. The Invite tab (see Figure 2-2) is
where you provide the email address (or
addresses) of the people with whom you want to
share a dashboard. You also can include an
optional message that you would like your
invitees to receive from the Power BI service. At
the bottom of the tab are the Allow Recipients
To Share Your Dashboard check box and the
Send Email Notification To Recipients check box,
which are self-explanatory. If you decide to send
an email as a notification to recipients, Power BI
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will automatically include a link to open the
dashboard in the email that is sent.
Notice in Figure 2-2 that when David types
Wendy’s name in the name box, Power BI
suggests her email address. Because Wendy is a
coworker in the same organization as David,
Power BI is able to offer these suggestions (more
details on this later). However, you also can type
an email address in this box if the email address
of the person with whom you are sharing does
not have the same domain name as your address
(for more details about this, read the section
“Inviting users outside your organization,” later
in this chapter). You can add more than one
person if required.
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Figure 2-2: In the Share Dashboard dialog box, you
specify the list of people invited and, optionally,
include a message for them.
If you clear the Send Email Notification To
Recipients check box (see Figure 2-3), you will
need to go on the Shared With tab to copy the
link to your dashboard that you will then send to
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recipients after you click the Share button. The
email addresses you provide will give those
accounts access to your dashboard, but those
individuals will not receive an email notification.
Figure 2-3: You can share a dashboard by copying a
link instead of sending an email from the Power BI
service.
On the Access tab (see Figure 2-4), the
Dashboard Link box contains the URL for the
dashboard. If you choose to not send a message
via the Power BI service to your recipients or you
simply prefer to use your own email account to
do so, you will need to copy this URL and send it
to your invitees. Using your own account can be
helpful when you want to ensure that your
recipients recognize that the incoming email is
from you, as opposed to seeing “noreply@powerbi.com,” which they, or their email
client, might filter out as spam. Also on the
Access tab is a list of users with whom you have
shared the dashboard, along with their assigned
privileges.
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Figure 2-4: The Access tab shows the users who
have access to the dashboard and provides a link to
share it.
The email message that Wendy receives looks
similar to that shown in Figure 2-5. If she has not
previously used Power BI, when she clicks the
link to open 2015 Sales.xlsx, she will be directed
to the Power BI website where she will need to
register with the service, just as David had to do
when he first used Power BI (see Chapter 1 for a
refresher on getting started with Power BI). If she
is already enrolled as a Power BI user, she will go
straight to the dashboard that David is sharing
with her.
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Figure 2-5: Power BI sends an email message to
users invited to share a dashboard.
Now, Wendy is looking at the same dashboard
as David, including the reports underlying the
visualizations pinned to the dashboard. However,
Wendy cannot modify either the dashboard
layout itself or any of the single reports; at this
point, she has read-only permission. For David to
make it possible for other users to edit his
dashboards and reports, he needs to create a
group workspace, which we will cover a bit later
in the chapter.
After David invites Wendy, she can open the
dashboard in its own Power BI session.
Dashboards shared by another user appear on a
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guest user’s Workspace pane with a “shared”
icon adjacent to the dashboard name, as shown
in Figure 2-6. This indicates that the dashboard is
read-only. So in this case, Wendy cannot change
or modify the content of 2015 Sales.xlsx, but she
can interact with reports pinned to the
dashboard and can open the reports underlying
each visualization by simply clicking a
visualization. Even if such reports are not listed in
the workspace, Wendy can open them through
the shared dashboard, but she cannot modify
their content (the Edit Report feature is turned
off in these cases).
Figure 2-6: Shared dashboards display a “shared”
icon before the name in the list of dashboards.
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Sharing via content packs
An additional technique to share reports and
dashboards within an organization is through a
content pack, which is a set of datasets, reports,
and dashboards that a user can copy within his
personal workspace. David might consider
using this feature to deploy a report to other
users, but he does not use this system in our
scenario because it is a technique that is better
suited to distribute a set of predefined reports
and dashboards that other users can customize
in their personal copy. Content packs are not
designed to share reports between users in an
active way, as David needs at the moment.
Chapter 5 shows you how to create and
consume content packs from the public gallery
and within an organization.
Inviting users outside your
organization
Thus far, David has invited Wendy to view his
dashboard; she works in the same company and
has an email with the same domain (@contosobi.com). But what happens when David wants to
invite someone who is not part of the same
company? Answering this question requires
some explanation.
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Although Power BI is designed for you to share a
dashboard with users who are within the same
organization, you can also share dashboards
with people from other organizations. The way
Power BI identifies “an organization” can be
described as follows:
Every user requires an email address within
the domain of the company.

Power BI does not accept generic email
domains such as hotmail.com, gmail.com,
and so on. Your company needs a unique
domain name, and all of the users must have
an email address within that domain. All of
the users having an email within the same
domain are considered part of the same
organization.

If you use Microsoft Office 365 and/or
Microsoft Azure Active Directory, you might
have different domains belonging to the
same organization. This is the only case for
which users having email with a different
domain name belong to the same
organization for Power BI.

Note If you are not sure whether your
organization already uses Office 365 and Azure
Active Directory, ask your IT administrator, and
if he would like to read more technical details
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C H A P T E R 2 | Sharing the dashboard
about authentication in Power BI, refer him to
the following documents:
https://powerbi.microsoft.com/documentation/
powerbi-admin-power-bi-security and
http://go.microsoft.com/fwlink/?LinkId=619090
(which downloads the Power BI Security white
paper)
On the surface, that seems rather restrictive, but
in reality, you also can share a dashboard with
users in other organizations, using the same
method as that described in the previous
section. However, when you specify an email
address with a domain other than that of your
organization, you will see a message similar to
one shown in Figure 2-7, which David receives
when he tries to share a dashboard with a
vendor.
Figure 2-7: The message that displays when you try
to share a dashboard with someone outside of your
organization.
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It is important to understand the difference
between a user within your organization (internal
users) and outside of it (external users):


Internal users You can invite internal users
to share a dashboard by email or by sending
them the URL of the dashboard. In the latter
case, users must be authorized. If a user
does not have authorization, she can ask for
permission when she clicks the dashboard
URL.
External users You can share a dashboard
with external users only by inviting them by
email. When an external user receives the
email, she must sign in to Power BI using the
same email account used in the invitation. If
she never previously used Power BI, she can
create a free account the first time she signs
in.
Finally, you can publish a report (but not a
dashboard) on the web. To do so, select the
report, click the File menu, and then click Publish
To Web, as depicted in Figure 2-8. In the Embed
In A Public Website (Preview) dialog box, click
Create Embed Code. This creates a public
webpage that anyone can visit. Keep in mind,
though, that you cannot control who can see
such a report, meaning anyone who has the URL
can view your data. For this reason, you should
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use this technique only when you want to
publish information intended for public
consumption; for example, a report embedded in
the public website of your company.
Figure 2-8: The File menu includes the Publish To
Web command, which makes a report available on the
Internet.
The Publish To Web feature guides you in
creating a public webpage, getting a URL that
you can send in an email, or the HTML code
required to embed the report in a page of a
website you own. For more technical information
about publishing a report to the web and to get
a detailed step-by-step guide, go to
https://powerbi.microsoft.com/documentation/p
owerbi-service-publish-to-web/.
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Creating a group
workspace in Power BI
Let’s return to David and Wendy. After David
invited Wendy, he realizes that he will need to
repeat the same share operation for every
dashboard he creates. Moreover, as soon as
other people become involved in the budgeting
process, he will need to send them the invitation
for all of the dashboards he shares with the
group. Fortunately, David discovers that he can
create a group of users with whom he can
automatically share all of his dashboards, and
also provide editing rights to certain users within
that group. By creating groups of users in Power
BI, you increase the level of collaboration among
them.
The only caveat is that you must have a Power BI
Pro license to have access to the group feature;
you cannot create groups by using the free
version of Power BI. However, you can try Power
BI Pro for 60 days free of charge, giving you an
opportunity to evaluate this feature and
determine whether it is good for your company.
Assuming that you—and David’s organization—
opt to purchase a Power BI Pro license, to create
a group, in the My Workspace pane, immediately
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below the list of workspaces, click the “+” button
to the right of Create A Group, as demonstrated
in Figure 2-9. (You might need to click My
Workspace to open that pane.)
Figure 2-9: You can create a group by clicking the
plus symbol (+) to the right of Create A Group.
David clicks the “+” button, which opens the
Create A Group dialog box in which he creates a
group named Budget 2016. This group will
initially include himself as administrator and
Wendy as a member. In the Privacy section,
shown in Figure 2-10, you can define the privacy
levels of the group.
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Figure 2-10: The group includes a list of members
and privacy settings that group administrators can
change later.
Every group has two privacy settings. The first
determines whether the group is visible only to
its members or also by other users within the
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organization who are not members of the group.
Here are the possible choices:


Private Only approved members can see
the results of the group’s activities.
Public Anyone can see what the group is
doing.
The second setting specifies whether all the
group members can modify the contents of
reports and dashboards. There are two choices:

Members can edit Power BI content.

Members can only view Power BI content.
If you select view-only for this second setting,
only group administrators can edit dashboards.
With David having configured the group as
shown in Figure 2-10, Wendy will be able to edit
the content of dashboards and reports included
in the group. David and Wendy will see the
public group in their list of group workspaces, as
illustrated in Figure 2-11.
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Figure 2-11: The list of group workspaces includes all
the groups of which the user is a member.
Now that David has created a group, he can
create reports and dashboards in the group that
will be immediately visible by Wendy. However,
he must import the data for these reports in the
same group; he cannot move into the group
what he already created in his personal
workspace. Importing data and creating the
reports will require some time at this point,
repeating the same operation he has already
done. Thus, when you know you will work with a
team, it is a good idea to create the group at the
outset, and save yourself a lot of redundant work
later on.
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Turning on sharing with
Microsoft OneDrive for
Business
Before moving forward, David wonders whether
he will be able to share the data sources, not just
the results (reports and dashboards). In
particular, he wants to allow other colleagues to
enter their data in Microsoft Excel files, so that
he will be able to create a budget using data
from workbooks modified by a number of other
people.
In Chapter 1, David was forced to copy the
budget data from Excel files received by
country/region managers, and then he split
those values by month in the table used to feed
his Power BI report. Now, he wants to give other
users the ability to modify the content of that
Excel file directly so that he does not need to do
all that work himself. For this reason, David
creates a single Budget table in another
worksheet of the same Excel file and copies to it
the budget values by Country/Region and Brand,
as depicted in Figure 2-12.
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Figure 2-12: David’s budget table now contains at
least one row for each Country/Region and Brand.
David created a formula in the table used by
Power BI that allocates the budget value over 12
months. At this point, David wants to share this
workbook so that his colleagues will be able to
directly modify the content of the budget table,
and this should automatically apply the new
values to reports and dashboards published in
Power BI.
Using OneDrive for Business is the best way to
share his source Excel workbook with other
colleagues. You might already know OneDrive,
which is the personal service with which you can
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store files in the cloud. But, even though you can
share files on OneDrive, there are limitations
when using it as a data source for Power BI,
especially when it entails automatic data refresh.
OneDrive for Business removes those limitations
and provides more control, as well. Moreover,
OneDrive for Business is integrated with Office
365 and directly supports groups, making it
easier to share documents across your
organization. To access OneDrive for Business, in
the upper-left corner of the Power BI site, click
the yellow button with the nine small squares in
it (see Figure 2-13), and then click the OneDrive
tile.
Note OneDrive for Business is a feature
included in Office 365. If you do not have an
Office 365 plan, you can subscribe to OneDrive
for Business separately, without activating
Office 365. If you are interested in using this
feature, contact your IT administrator to
determine which licensing option better fits
your requirements.
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Figure 2-13: You can access OneDrive by clicking the
button in the upper-left corner of the Power BI website
and then clicking the OneDrive tile.
On the OneDrive webpage that opens, David
uploads the workbook to the Budget 2016 group
he previously created to share reports and
dashboards. A group defined in Power BI
corresponds to a group in Office 365, so you
have an associated OneDrive for Business folder
where you can place files to share. Figure 2-14
shows the sequence of steps that David needs to
do to upload the document.
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Figure 2-14: The actions required to upload a
document in a group folder in OneDrive for Business.
When David finishes uploading the file, he can
see it in the list of the files of the Budget 2016
folder, as depicted in Figure 2-15. Also in the
figure, there is a Sync button which you can use
to get information about how to synchronize the
folder with a local computer, so that you can edit
the file in a local folder of your PC and
automatically upload any update to the shared
folder on OneDrive. Thus, all of the files available
in this OneDrive folder can now be shared
among members of the Budget 2016 group, and
the files will be available as a possible data
source for reports created in the corresponding
Power BI workspace.
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Figure 2-15: After upload, the “Sales 2015 and
Budget 2016” file is available and listed in the Budget
2016 group folder.
David has now shared a file to the Budget 2016
group. Later, he will ask other group members to
update their budget data themselves. Before
doing that, David wants to prepare a report that
will display the aggregated total of the budget
for every country/region, comparing it with the
sales made in previous years.
Going back to Power BI, David opens the list of
workspaces available (refer to Figure 2-11) and
selects Budget 2016. Power BI displays
dashboards, reports, and datasets for that group.
Of course, initially all of these lists are empty, as
shown in Figure 2-16.
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Figure 2-16: The initial list of dashboards, reports,
and datasets is empty for a new workspace group.
David wants to create a dashboard and a report
based on data stored in the Excel file that now
resides in the group folder on OneDrive for
Business. The approach is similar to what he did
when he made his first foray into Power BI (see
Chapter 1), but instead of uploading a file from
his own local computer (Local File), this time
David selects the OneDrive tile to specify his
data source, as shown in Figure 2-17. Notice,
though, that the name on the tile is “OneDrive –
Budget 2016.” Because David is using the Budget
2016 workspace, the associated OneDrive folder
is automatically proposed as a possible data
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source. From a Power BI perspective, the biggest
difference between a local file (one stored on
your computer) and a file on OneDrive (stored in
the cloud) is that the former cannot
automatically update a report based on it,
whereas the latter can propagate changes to
data to Power BI reports without user
intervention.
Figure 2-17: Possible sources of files for Power BI
include local files and OneDrive.
After David selects OneDrive – Budget 2016, a
message asks him to select the file to which he
wants to connect to Power BI. There is only one
file in this folder, so David clicks it and then
clicks Connect, as shown in Figure 2-18. The file
selected is highlighted in a different color so that
it is recognizable when there are multiple files
available.
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Figure 2-18: The list of files available in OneDrive for
Business.
After choosing the Excel file on OneDrive for
Business, David must then decide how he wants
to use that file. There are two options (see Figure
2-19): Import Excel Data Into Power BI, and
Connect, Manage And View Excel In Power BI. If
you want to use the Excel file just as a “raw” data
source for your reports, select Import Excel Data
Into Power BI. Or, you might prefer to copy an
existing Excel file as is, using both the data
model (if you have one in Power Pivot) and all of
the Excel features, such as PivotTables,
PivotCharts, and other visualizations available in
Excel. If this is the case, choose the Connect,
Manage And View Excel In Power BI option. You
will see practical examples of this choice later in
this book.
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David selects Import Excel Data Into Power BI by
clicking the Import button in that section.
Figure 2-19: Click the Import button to bring in the
content of an Excel file stored in OneDrive for
Business.
David now has a dataset available, named Sales
2015 And Budget 2016. The dataset contains two
tables, Sales and Budget2016, because the Excel
file imported has two worksheets with one table
each. The Budget2016 table (refer to Figure 212) is the one that needs to be modified by other
managers, inserting updated numbers for their
individual budgets. The Sales table is still the
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same as it was in Chapter 1 (refer to Figure 1-29),
in which David has updated only the Budget
column using a formula that searches the
corresponding value in the Budget2016 table
and allocates it by month. Thus, when a manager
updates a row in the Budget2016 table, Excel
automatically updates the Sales table.
Using the new dataset, David creates a report by
dragging available fields to the report’s central
pane. The goal of this report is to show a quick
recap of the overall budget divided by brand and
country/region. For this reason, David chooses a
matrix visualization in which he inserts fields from
the Sales table (Brand on rows, CountryRegion
on columns, and Budget on values, as shown in
Figure 2-20). While doing this, he realizes that
the two tables have identical fields, and this
could be confusing. Because the budget is
allocated in the Sales table, it would be nice to
hide the Budget2016 table from the Fields list.
When you import an Excel file, all of the tables
become part of the Power BI data model and are
visible. Later on in the book, you will see how to
control the Power BI data model in more detail.
For the moment, David just wants to create a
first report, and he knows that using the fields
from the Sales table is the right choice because
he can also create a clustered column chart,
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below the matrix, that compares the budget with
the sales of previous years.
Figure 2-20: The Budget Totals report has both
matrix and clustered column chart visualizations.
David saves the new report as Budget Totals and
he also pins both visuals to a new dashboard
with the same name. David already included
Wendy in the group, so he sends her an email
asking for a review and edit of budget numbers
for Germany (Wendy is the country/region
manager for Germany).
Wendy receives David’s email, signs in to Power
BI, and sees the dashboard. She chooses the
Budget 2016 workspace, which she can see
because David added her as a member of the
group. She opens OneDrive and clicks the Sales
2015 And Budget 2016 file name. Now, she can
see the content of the workbook in her browser,
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and she chooses the Budget worksheet. Next, on
the menu bar, Wendy clicks Edit Workbook and
then clicks Edit In Excel Online to edit the file in
her browser, as illustrated in Figure 2-21.
Figure 2-21: Wendy can edit the Excel workbook
stored in OneDrive for Business in her browser.
Note It is worth remembering that Excel
Online is available on many platforms. This
means that Wendy can edit the workbook
directly on her iPad.
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Wendy corrects the budget for Germany for the
A. Datum and Contoso brands (see Figure 2-22),
because she knows that different marketing
conditions and product lifecycles will affect the
previous estimation.
Figure 2-22: The numbers entered in Excel Online
are immediately stored, updating the underlying
workbook in OneDrive.
Wendy wants to see whether the new values
have been correctly allocated, so she clicks the
Sales worksheet and sees that the new value
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(15,000) for the A. Datum brand in Germany is
divided into 12 identical parts (1,250 each) for
each month, as shown in Figure 2-23. She makes
a mental note that such an allocation does not
correctly represent the seasonality of certain
products, so she will discuss this issue with David
during the next meeting.
Figure 2-23: The Sales table allocates the budget by
month in the Budget column.
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Now, Wendy goes back to the Budget Totals
dashboard in Power BI and can see the updated
data (see Figure 2-24) correctly represented in
the matrix. She also can see that all of the other
totals are updated, too. Wendy is pleased and
feels confident that the reports built by David
will be accurate.
Note The Power BI report and dashboard
might exhibit some latency when displaying
data that was updated in files residing on
OneDrive. For performance reasons, the reports
are not refreshed every second; thus, if you try
to do the same operation, you might not see
updated numbers immediately when you go
back to Power BI from Excel Online. However, if
you wait a few minutes, you will likely see
updated numbers. You also might need to
refresh the page in the browser to see those
updated numbers.
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Figure 2-24: The dashboard in Power BI
automatically updates numbers when someone
modifies the underlying Excel workbook stored in
OneDrive.
You have seen how David created a collaborative
environment in Power BI by using groups and
OneDrive for Business. Such a collaboration
requires licenses for Power BI Pro and OneDrive
for Business (remember that an Office 365
subscription usually includes OneDrive for
Business, too).
Using groups, you can share files in OneDrive for
Business only with group members. If you want
to share a file with someone outside the group,
you must use the personal workspace in Power
BI and the personal folder in OneDrive for
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Business. Remember: only group members can
see everything you store in a group.
If you do not have OneDrive for Business
available, you can still use the personal edition of
OneDrive (which is available at no charge), but
the scenario described in this chapter will be
slightly different. You can share an Excel
workbook in OneDrive, and other people can
edit its content if you share the workbook with
them. However, you can import such a workbook
only to the personal workspace in Power BI, not
to a group workspace. Thus, it is possible to do
the following:




Create an Excel workbook on OneDrive.
Share the Excel file with other users, even
those outside your organization (there are
several limitations when doing so with
OneDrive for Business).
Import the Excel file stored on OneDrive in a
Power BI dataset.
Use the dataset in your reports and
dashboards. However, you can share them
only within your organization, not external
users.
You can use the personal OneDrive to create a
very similar scenario, but you will not gain the
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benefit of automatic visibility of files to group
members as you do by using OneDrive for
Business.
Viewing reports and
dashboards on mobile
devices
All users who can access Power BI can see all of
the reports and dashboards, even on native
applications for mobile devices. There are native
Power BI apps for Windows (available at the
Windows store), iOS (available at the Apple
store), and for Android (available at Google Play).
These native apps also have additional features,
such as annotations. They are updated with new
features regularly.
Getting back to David, he might, for example,
want to show the dashboard on his Windows 10
tablet device to a colleague during a meeting.
Because the meeting room does not have good
Internet connectivity, he takes advantage of the
offline availability of dashboards by using the
mobile app. This feature also makes it possible
to view reports offline, even if you have no
interactive capabilities. Thus, David can see the
data available with the last refresh of the report,
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and this is enough for the meeting he planned.
Figure 2-25 shows that the rendering of the
Budget Totals report is pretty similar to the one
available in the browser.
Figure 2-25: Rendering of a report in the Power BI
app for Windows 10.
You might also experience different visual
presentations in the Power BI app on tablets and
smartphones. For example, Figure 2-26
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demonstrates how an iPad displays the
dashboard created by David in Chapter 1. You
can zoom in to each visualization and navigate
with the benefit of larger graphics, making it
easier to read numbers, as illustrated in
Figure 2-27.
Figure 2-26: Rendering of a dashboard in the Power
BI app for iPad.
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Figure 2-27: An enlarged view of a dashboard’s
visualization in the Power BI app for iPad.
That same dashboard is rendered differently on
the smaller screen of an Android smartphone, as
depicted in Figure 2-28. Visualizations are
organized in a vertical column, and you can
zoom in to each one, as shown in Figure 2-29.
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Figure 2-28: Rendering of a dashboard in the Power
BI app for an Android smartphone.
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Figure 2-29: An enlarged dashboard visualization in
the Power BI app for an Android smartphone.
Users who need to enter budget data in Excel
files can also choose to use the Excel app on
mobile devices. This app provides a user
interface that is optimized for the device, which
offers a better user experience than the generic
web user interface of Excel Online. Figure 2-30
and Figure 2-31 show how the two worksheets
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C H A P T E R 2 | Sharing the dashboard
of the Sales 2015 And Budget 2016 workbook
display on an Android smartphone.
Figure 2-30: The Excel app on an Android
smartphone, displaying the Sales worksheet.
Figure 2-31: The Excel app on an Android
smartphone, displaying the Budget worksheet. Users
can enter data here.
Different operating systems and devices might
provide a slightly different user interface. The
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C H A P T E R 2 | Sharing the dashboard
goal here is just to inform you of the options
available for tools that complement the Power BI
service and app. We suggest that you try these
applications for yourself, with your own data, so
that you can evaluate which of your data for
Power BI can be displayed/edited using a mobile
device.
Conclusions
In this chapter, you saw how to share
dashboards, reports, and raw data with people
within an organization.



101
You can invite a user within your
organization to see a dashboard. That
person can also see the reports underlying
the dashboard.
To share a dashboard with users outside of
your organization, you must provide them
an account within the same tenant.
You can publish a report on a webpage;
however, anyone can view that report. You
should consider this option only for data
intended for public consumption; you should
not use it for sensitive, company-private
data.
C H A P T E R 2 | Sharing the dashboard



102
You can create groups of users within your
organization and share datasets, reports, and
dashboards.
You can use the same groups with OneDrive
for Business to make it possible for other
users to directly enter data in shared reports,
which are then automatically updated after
each change.
You can display dashboards and reports on
mobile devices by using native apps. These
apps typically provide a better user
experience if you are using smaller displays
and have limited offline access to
dashboards.
C H A P T E R 2 | Sharing the dashboard
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CHAPTER
3
Understanding data
refresh
Let’s continue following David, the
manager of budgeting at Contoso.
While he explores Microsoft Power
BI, he now has begun sharing his
dashboards and reports with
Contoso’s country/region
managers around the globe.
Everybody likes the idea of being
able to view a report on any device
and share considerations while
looking at the same figures.
Nevertheless, the managers are
concerned that they are making
decisions based on sales as of
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C H A P T E R 3 | Understanding data refresh
October 2015, and it is now midDecember. The figures are no
longer the best data upon which to
forecast sales, a fact that is even
more pertinent for the products
that show a clear seasonality.
Thus, David needs to retrieve the
latest sales data set and refresh
the Microsoft Excel model. Urged
on by the country/region
managers, he ends up doing this
each and every morning until he
begins to wonder whether this
process can be automated in
some way. In fact, Power BI offers
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C H A P T E R 3 | Understanding data refresh
an option with which he can
schedule refreshes.
Introducing data refresh
In Chapter 1 and Chapter 2, you learned the
basics of Power BI: how to upload a workbook
containing some data, build reports and
dashboards, and how to share the content with
other users in your organization or outside of it.
Those chapters also touched upon the basics of
data refresh: uploading a new version of a
workbook containing data, and using Microsoft
OneDrive for Business to automate the
uploading process.
For both of these scenarios, updating data
meant that David needed to refresh the content
of the Excel file manually and then upload it to
Power BI, either via another web service, such as
OneDrive for Business, or by using the Power BI
user interface (UI). When it comes to refreshing
your data, Power BI offers much more control.
Learning it requires some attention to details.
We suggest that you read this chapter in its
entirety because there are some small but
important details that you need to know before
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C H A P T E R 3 | Understanding data refresh
making any decision about your future data
refresh strategy.
More important, in the first two chapters, we
kept the presentation deliberately simple, trying
to show only the basic concepts. In this chapter,
however, we begin to delve into the details of
working with Power BI. Hereinafter, the details
become important.
Anyway, first, we need to focus on what “refresh”
really means. In the context of this chapter,
refreshing data does not mean to manually
update the Excel workbook and save it as
another version of the same file. Instead, we
want the workbook to automatically update its
content by using a connection to the source
database from which we originally query the
data.
Let’s review the steps in David’s simple dataprocessing system:
1. Data is retrieved from the database by IT. IT
then gives David an Excel file containing the
latest figures of sales.
2. David manually copies the information to his
own version of the Sales 2015 workbook.
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C H A P T E R 3 | Understanding data refresh
3. He saves it so that OneDrive uploads the
content to the cloud.
4. Power BI loads the content of the file from
OneDrive and updates its own internal data
model.
As part of step 3, the Excel file automatically
computes the forecasts generated by the
country/region managers by using formulas.
These results are saved in OneDrive by the
managers, so the results are immediately
available to Power BI, too.
The data refresh mechanisms David learned so
far are useful to automate step 4. But now, he
wants to automate steps 1 and 2. This requires
some more understanding of how Power BI
works internally.
Introducing the Power
BI refresh architecture
What happens when you upload a workbook to
Power BI? Let’s consider the workbook that
David uploaded to look more closely at the
process. Recall that his workbook contained a
table. Figure 3-1 shows the flow of data.
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C H A P T E R 3 | Understanding data refresh
Figure 3-1: The data flow, from the original database
up to Power BI.
David’s data moves from SQL Server into Excel
(David does that). Next, the Excel file is uploaded
to OneDrive where Power BI reads it. After
reading the file, Power BI generates a SQL Server
Analysis Services (SSAS) database that ultimately
computes, through the Power BI UI, the
dashboards and reports. Sounds complicated,
right? In fact, it is, but luckily, Power BI hides all
of this complexity, making it easy for you to
generate reports from Excel files. Nevertheless,
to understand how data refresh works, you need
to have a clear picture of the complete flow of
information.
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C H A P T E R 3 | Understanding data refresh
To use data refresh, you need a way to pull data
from the data source (SQL Server, in this
example) and push it directly into the SSAS
model generated by Power BI. In other words,
you want to create a data flow that circumvents
Excel and OneDrive (both operations are done
outside of Power BI) to make it flow as shown in
Figure 3-2, in which the steps that we want to
remove appear in a blue box.
Figure 3-2: We want to remove the steps enclosed in
the blue box to make data refresh work more
efficiently.
Here is what you need to do to make this
happen:
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C H A P T E R 3 | Understanding data refresh


The data set cannot be a plain Excel table,
because Power BI needs to know how to
query the source database (SQL Server, in
this example) in order to refresh the data.
Obviously, it cannot rely on asking you or
asking IT. The method must be formalized
and use a language that Power BI can
understand.
The SSAS engine running in Power BI needs
a way to access the source database. Such a
database is usually located within your
company (or on your laptop). Thus, you will
need software that implements the
connection with Power BI.
Be sure that you understand these architectural
requirements well before moving on with the
rest of the chapter. As you will see by reading
the next sections, we stripped away most of
the technical complexities that comprise data
refresh, but you need to keep in mind that the
aim is to create the scenario depicted in
Figure 3-2.
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Introducing Power BI
Desktop
You might remember from Chapter 1 that Power
BI offers you two ways of interacting with it:
direct access to the web service, or by using
Power BI Desktop. In this section, we are going
to show you how to use Power BI Desktop.
Power BI Desktop is an application that runs
locally on your computer but offers you all of the
features available on the web, plus many more
options for building a data model.
By using Power BI Desktop, you do not rely on
the web service to create the data model for you.
Instead, you have the full modeling capabilities
of Power BI available at your disposal and
control.
Why should you worry about Power BI Desktop
at all if the web service is capable of building a
model for you? There are several reason for this,
but for now, let’s concentrate on one of those
reasons: you can describe the details of your
dataset, which accomplishes the first
requirement of our data-refresh scenarios from
the preceding section. But, before we can get to
that, first you need to download Power BI
Desktop from the Power BI website and install it.
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On the Power BI website, on the right side of the
menu bar at the top, click the download button
(see step 1 in Figure 3-3), and then click Power BI
Desktop (see step 2).
Figure 3-3: Downloads in Power BI are available in
the download list, on the right side of the menu bar.
After you have downloaded Power BI Desktop,
install it by following the instructions provided in
the Microsoft Power BI Desktop Setup Wizard,
and then start the application. A welcome screen
greets you, and then you see the main Power BI
Desktop window, as shown in Figure 3-4.
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Figure 3-4: The Power BI Desktop UI resembles the
Power BI website.
The first thing you will probably notice is that the
UI of Power BI Desktop is very similar to that of
Power BI. Nevertheless, as you will learn, there is
more to Power BI Desktop, and it is a bit more
complex to use than its web-based counterpart,
but it exploits the full power of the Power BI
engine.
As with Power BI, the first step is to provide
some data to Power BI Desktop. Because the
original data is in Excel, you can begin practicing
with Power BI Desktop by using the same Excel
file you used earlier for the website. On the
ribbon, in the Data group, click Get Data, and
then click Excel (see Figure 3-5).
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Figure 3-5: Your first step when using Power BI
Desktop is to load some data; in this example it’s from
Excel.
In the Open dialog box, select an Excel file and
then click Open. The Navigator dialog box
opens, in which you select the source file. When
you load data from Excel, you can choose to load
from tables or from worksheets. Figure 3-6
shows the two sample items available. Note that
for this exercise we renamed the worksheet
“Sales” to “Sales Worksheet” to make the figure
clearer. In your models, it is likely they will have
the same name, and only the icons adjacent
to the names will differentiate tables from
worksheets. For this example, let’s work with
the table.
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Figure 3-6: The Power BI Navigator helps you to
choose the source for importing data in Power BI
Desktop.
Select Sales, click Load to import the table into
Power BI Desktop, and then close the Navigator
dialog box. At this point, you will likely feel at
home and in familiar surroundings. In fact, by
using the Fields and Visualizations panes, you
can build a report in Power BI Desktop in the
very same way you built the report earlier, on the
website.
For example, Figure 3-7 shows that you can build
a report similar to the one you built on the
website. The main difference is that, now, you
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C H A P T E R 3 | Understanding data refresh
are doing the work directly on your PC instead of
interacting with a cloud service.
Note We suggest that you become familiar
with the UI of Power BI Desktop by
experimenting with some reports. In this
chapter, we are not showing you a step-by-step
guide to Power BI Desktop. Instead, we focus
on the new features available in Power BI
Desktop that are not in the cloud service.
Figure 3-7: This is a sample report that you can build
by using Power BI Desktop.
Building the report, you can appreciate how
user-friendly the Power BI Desktop environment
is, with features such as copy-and-paste
available. So, for example, if you need a
visualization similar to one you have already
created, copy it, paste it, et voilà: the job is done.
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Of course, there is more to it than that, but
Power BI Desktop offers little things like this that
make life much better.
Publishing to Power BI
Let’s get back to our exercise. When the model is
ready, save it on your computer using the name
Sales PBD. Of course, at this point, the report is
local to your PC, and no one else can view it.
However, you ultimately want to publish this
model on the Power BI cloud service, to take
advantage of all the features of Power BI,
including sharing and viewing on a mobile
device.
To do this, go to the Power BI Desktop ribbon,
and then, on the Home tab, in the Share group,
click Publish. Power BI Desktop then asks you to
sign in to the Power BI service (and might ask if
you want to modify your changes). As previously
mentioned, Power BI Desktop is an application
that runs locally on your PC, and it can work
without a Power BI account, but as soon as you
want to publish your data, you need to have an
account (or create one) and sign in. After you
sign in, Power BI Desktop shows the message
depicted in Figure 3-8, confirming the operation
and providing a link to the published report.
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Figure 3-8: When the model is published, you can
immediately see it using the appropriate link.
If you open the file in the Power BI website, you
will find a dataset named Sales PBD and a report
with the same name. By using Power BI Desktop,
you created both a model and a report, and,
when publishing it to Power BI, it created both
objects.
Note The model is copied from your local file
to Power BI. When the model is in Power BI,
you can further enhance it, but the two versions
are disconnected. Changes that you apply to
the published model from within your browser
are not be applied to your local version on your
PC, and any subsequent publish operation that
you initiate from Power BI Desktop will
overwrite the changes that you made via the
web browser. Thus, when you begin building
models with Power BI Desktop, it is a good
practice to continue updating them locally and
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then republish them. Do not modify the online
version. You might want to use different version
names for your local files in order to publish
different, varied reports in the Power BI cloud
service.
After you get used to this method of developing
reports, you will find it extremely convenient. In
fact, working with Power BI Desktop is more
productive because you do not need an Internet
connection and you have the full power of a
Windows application. When the model is ready,
you publish it, overwriting any previous version
that you might already have done.
Let’s recap what we have seen so far:



Power BI Desktop is a Windows application
that offers the same features of the cloud
service, but it runs on your local PC.
You can build a model with Power BI
Desktop and save it to your PC.
You can publish a Power BI Desktop model
to Power BI, but you’ll need to have or create
an account and sign in first.
You might remember that this chapter is about
data refresh. Why is Power BI Desktop relevant
to data refresh? A Power BI Desktop file contains
all the information needed to refresh the model.
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In fact, in the Power BI Desktop file, we created a
link between the original Excel file containing
figures for the budget and the Power BI Desktop
model.
In Chapter 1, when we uploaded the file to
Power BI, we simply copied it. However, using
Power BI Desktop, we create a link between the
Excel file and the Power BI Desktop file by
writing a query. In reality, we do not actually
author any query, but as you will learn, Power BI
Desktop created the query for us, making the
task transparent.
All that is missing at this point is to provide a
way for Power BI to access the Excel file that is
the ultimate source of our data. In fact, the
original Excel file is stored on the local PC, and
the Power BI cloud service cannot access it.
Solving this problem is the topic of the next
section.
Installing the Power BI
Personal Gateway
The Power BI Personal Gateway is another piece
of software that can connect with the Power BI
cloud service and carry out the queries stored in
the Power BI Desktop file. You can download it
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from the same webpage from which you
downloaded the Power BI Desktop application,
but this time select Power BI Gateways after you
click the download button. Power BI then asks
you to choose between the two Power BI
gateways:
Personal Gateway This version is intended
for use with personal datasets. It is simple to
use and install but offers limited features
regarding monitoring and security for
multiple users.

Enterprise Gateway This version offers
more functionality but, at the same time,
involves more complexity in its setup and
usage and usually requires involving your IT
department.

Note In the last chapter of this book, we briefly
outline the differences between the personal
and the enterprise versions of the gateway. For
the purposes of this discussion on how data
refresh works, those differences are negligible.
Let’s go back and visit David to see how he is
progressing.
David is still experimenting with Power BI, so he
has no intention of installing a complex system.
For this reason, he chooses the Personal
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C H A P T E R 3 | Understanding data refresh
Gateway. Before jumping into the setup of the
Personal Gateway, though, he needs to
understand how it will be implemented.
If David runs the setup with administrative
privileges (that is, he installs it as an
administrator), the gateway will run as a service.
On the other hand, if he installs it as a standard
user, the gateway runs as a normal program.
What is the difference? When the gateway runs
as a service, it runs even when no user (or
another user) is signed in to his PC. Conversely, if
it runs as a standard program, the gateway will
run only if David is signed in to his PC. This
might be relevant if at some point he wants to
refresh his data while at home, accessing the
Power BI cloud service, but his laptop is still in
the office, turned on but without anybody using
it. In such a scenario, if the gateway runs as a
service, the refresh operation will succeed,
whereas if it runs as a normal program, it will fail.
The choice of which to run is up to you. Figure 39 shows David choosing to run the installer as an
administrator.
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C H A P T E R 3 | Understanding data refresh
Figure 3-9: Right-click the downloaded installer to
install the gateway as an administrator.
After you install the gateway, you must start it to
complete the configuration. In fact, when you
start it, it requires the credentials to access
Power BI. The gateway requires them because it
must contact the Power BI cloud service and
begin answering queries coming from the
service.
After David provides the correct sign-in, the
Power BI Gateway – Personal dialog box opens,
in which he must provide another set of
credentials, as illustrated in Figure 3-10.
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C H A P T E R 3 | Understanding data refresh
Figure 3-10: As part of the configuration, the gateway
asks for the credentials to use when running as a
service.
Why does the gateway require these credentials?
Because David wants to run it as a service. Being
a service, it will run even if no one is connected
to the PC, and it requires credentials to access
files and connections from David’s PC. In other
words, the gateway will have the same
permissions that David has on his PC, and those
are required to let it access his files and data sets
as if it were him.
When everything is complete, the gateway
informs you that one operation still remains: you
need to go to powerbi.com to complete the
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C H A P T E R 3 | Understanding data refresh
setup of the data sources. When you go there,
you will reenter your credentials.
Note At first sight, it might seem cumbersome
to be required to enter the credentials so many
times. Indeed, it is important that you follow
the procedure in the correct way. The gateway
connects the Power BI website to your PC, and
it will be able to access all of your files. Security
is always important, and Microsoft takes it very
seriously.
David is now near the end of the gateway
configuration. The last step is to visit the Power
BI cloud service. To complete the setup, in Power
BI, on the menu bar, he clicks the configuration
button (the small gear icon; step 1 in Figure
3-11) and then chooses Settings (step 2). This
opens the Settings page on which he can
configure the settings for each data set, as
demonstrated in Figure 3-11.
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C H A P T E R 3 | Understanding data refresh
Figure 3-11: The final step in the configuration of the
gateway is to grant permission for a Power BI dataset
to access it.
On the Settings page, David sees an alert with
two important pieces of information:


The gateway is online and running (in this
case) on a PC named HARRY (which you can
see in Figure 3-11, in the line below the
Gateway Status section header).
This data set is not yet ready. In fact, even if
the gateway is set up, you need to specify
the credentials specific for the single data
source.
Why do you need to enter credentials again for
each data source? The reason is that every data
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C H A P T E R 3 | Understanding data refresh
source might require different user credentials.
In our example, the Sales PBD file is stored on
David’s local PC, so it is automatically accessible
by using the Windows credentials that David
stored in the Personal Gateway, too. In this case,
when David clicks Edit Credentials, he is asked to
choose an authentication method, as illustrated
in Figure 3-12. The Windows authentication
method is the only option, and when David clicks
Sign In, he will not need to provide his user
name and password again. However, for other
data sources not using Windows authentication,
clicking Sign In will require a user name and
password to make it possible for the Personal
Gateway to connect to that data source during a
refresh operation.
Figure 3-12: You have a choice of authentication
methods for each data source.
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C H A P T E R 3 | Understanding data refresh
Configuring automatic
refresh
After you set the credentials, the data source is
ready to be refreshed. Power BI now has all of
the information it needs to refresh the data, both
on demand and on a scheduled basis. Expanding
the Schedule Refresh section, you can define
when Power BI attempts to refresh the dataset.
Figure 3-13 shows an example of a data refresh
scheduled twice daily, at 9:00 AM and 4:00 PM.
Figure 3-13: You can configure automatic data
refresh to run daily or weekly and at different times.
Note You need a license for Power BI Pro to
schedule more than one refresh per day. Using
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C H A P T E R 3 | Understanding data refresh
the free Power BI license, you can schedule only
one daily refresh.
In case the refresh fails, you have the option to
receive an email alert so that you can take the
needed remedial actions.
At this point, you can either wait for the refresh
to happen or, if you want to be sure that
everything is set up correctly, you can force an
immediate refresh. To perform this operation, in
the navigation pane on the left, in the Datasets
section, click the ellipsis to the right of the data
source (Sales PBD, in our example; step 1 in
Figure 3-14), and then click Refresh Now, shown
as step 2 in Figure 3-14.
Figure 3-14: You can refresh a dataset immediately
by clicking Refresh Now.
When you ask for a refresh, Power BI prepares
for the data refresh and then starts it. Depending
on the size of the dataset and the speed of the
Internet connection, the time to carry out the
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C H A P T E R 3 | Understanding data refresh
refresh can range from a few seconds, as in the
case of David, to a much longer duration. You
can see when a dataset was last refreshed by
looking in the same window where you asked for
the immediate refresh (click the ellipsis to the
right of the data set).
Note Although it might be obvious, it is useful
to state an important fact: the data refresh
happened on the model in the cloud, not in the
model on David’s PC. In fact, if David opens
Sales PBD by using Power BI Desktop, he will
see the data as he saved it. Power BI does not
change any file on your PC, it only uses the
gateway as a medium to access the data sets
upon which the model is based.
Conclusions
In this chapter, you learned the basics of data
refresh. Let’s recap them briefly:


130
You can upload simple data models, based
on an Excel file, to Power BI and refresh
them by using the Personal Gateway, which
makes it possible for Power BI to access your
local datasets.
For data refresh to work on more complex
models—for example those that load data
C H A P T E R 3 | Understanding data refresh
from SQL Server—you need to use Power BI
Desktop.


With Power BI Desktop, you build models
containing the needed information to let the
cloud service connect to the Personal
Gateway and retrieve the dataset.
You can refresh your data daily with the free
license, whereas you need a professional
license if you need to refresh your model
multiple times each day.
Power BI Desktop offers many more features and
will let you move to the next level in the learning
path of data modeling. This is the topic for
Chapter 4.
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C H A P T E R 3 | Understanding data refresh
CHAPTER
4
Using Power BI
Desktop
In Chapter 3, we introduced the
concept of data refresh. Contoso’s
manager of budgeting, David, was
able to refresh a Power BI model,
based on a Microsoft Excel
workbook that contains sales for
the last three years and forecasts
for the next year. To perform that,
he had to learn the basics of
Power BI Desktop, a Windows
application that brings the full
modeling power of Power BI to
your desktop.
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C H A P T E R 4 | Using Power BI Desktop
In this chapter, David will move a
few steps further in building his
Power BI model. But, the solution
he has built so far still depends on
IT providing him with the sales
figures for the last three years.
Fortunately, David discovers that
Power BI Desktop can load the
numbers directly from the Contoso
data warehouse via Microsoft SQL
Server. Now, when Power BI
carries out data refresh, it will
automatically get the latest sales
data, updating the entire model.
Let’s take a brief look at what David needs to do:

133
Load sales figures from the data warehouse
instead of using his Excel file. This requires
accessing the corporate database, but the IT
C H A P T E R 4 | Using Power BI Desktop
department can give him access to the data
he needs.

Load forecasts for the next year from the
Excel file that the country/region managers
update every day.
Power BI Desktop offers all of the functionalities
that David requires. Now, let’s go deeper and
learn how to take advantage of this extremely
useful application.
Connecting to a
database
David already knows how to load data into
Power BI Desktop from Excel; he always did it
using the Excel file that he received from IT. Now
he needs to work from a database. To access the
database, he will ask the IT department to
provide him the credentials to query the
database and read the information he needs.
Karin, the database administrator at Contoso,
grants him read access to a view that returns the
same dataset that she has been sending him
every day.
Karin is happy to do so because she will no
longer need to fill the Excel workbook. If David
can load the data and manage to grab insights
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by himself, her daily list of chores will become
lighter. Thus, Karin tells David, “You can access
the view named Sales2015 using your Windows
credentials on the server ContosoDbServer.”
David’s account can only read that view, so there
is no potential danger. Database security will
guarantee that nothing bad can happen to the
database.
In Power BI Desktop, on the Home tab, David
uses the Get Data function, this time using the
SQL Server option. In the dialog box that opens,
he provides the connection information Karin
gave him (see Figure 4-1) and then clicks OK.
Figure 4-1: To connect to SQL Server, you need to
provide the server location.
Power BI Desktop connects to the database and
presents a list of data tables, which includes the
one that Karin created, Sales2015. When David
clicks it, Power BI Desktop shows a preview of
the content, as depicted in Figure 4-2.
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C H A P T E R 4 | Using Power BI Desktop
Figure 4-2: Use the Navigator to choose from among
the tables available on the database.
After David makes his selection and clicks Load.
Power BI Desktop asks how he wants to interact
with the data. He selects Import and then clicks
OK, as illustrated in Figure 4-3.
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C H A P T E R 4 | Using Power BI Desktop
Figure 4-3: Before importing data from an SQL
database, you need to choose the loading method.
Let’s take a moment to learn about this
connection option because it is an important
one and will help shed more light on how Power
BI connections work.
When you choose Import, Power BI Desktop
connects to the database, loads the information,
and stores it within its internal data model. You
can then work on your data in Power BI Desktop
without being connected to the database. You
will only need a connection when you want to
refresh the data.
With DirectQuery, Power BI Desktop does not
load the data into its internal database. Instead,
it runs a query to the original database every
time it needs to draw a chart or, in general, run a
query. Thus, the connection between Power BI
Desktop and the database will be permanent.
The contrast in the query timings reflects a key
difference: when you use Import, you are
working with data that is only as current as the
latest refresh, whereas with DirectQuery you
always see the latest information available when
you create the report.
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At first glance, it looks like DirectQuery is the
most convenient method for loading data, but
this is not totally true. If the data is updated
frequently, it is very likely that one minute you
will see a report with a set of figures, but when
you open it again a few minutes later, the
numbers might no longer be the same. This is
frustrating if you are analyzing information over
the span of an entire year (which is what David is
doing). Numbers that change too frequently can
become disturbing. Also, although real-time data
might sometimes be useful, it comes at the cost
of query speed; DirectQuery by its very nature is
much slower than working with data that is
resident on your device and directly accessible
by Power BI Desktop.
As a final note, keep in mind that DirectQuery
works fine when you use Power BI Desktop on
your laptop, but when you publish the model to
Power BI, the cloud service needs a way to
communicate with the internal database server.
This is accomplished by using the Enterprise
Gateway, which is the advanced version of the
Personal Gateway, to which you were introduced
in Chapter 3.
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Note Because this is an introductory book, we
do not discuss DirectQuery further, as it entails
some deeper technical details. Yet, we consider
it important, so we wanted to provide you with
a fundamental understanding of the choice and
its implications as well as the additional options
that are available when you use DirectQuery.
As mentioned just a moment ago, David wants
to analyze data over the span of a year, so he
does not need his figure to be updated
constantly; thus, he chooses Import and, after a
few seconds, the loading process finishes. He
notices that the name of the table is too long. In
fact, Power BI Desktop used the full name of the
table (including the schema name), but you can
easily give it a new name by right-clicking it and
then, on the shortcut menu that opens, select
Rename, as demonstrated in Figure 4-4.
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Figure 4-4: You can rename a table by using the
Rename command.
After he renames the table, there seems to be no
difference between this model and the previous
ones that he created by loading data from Excel.
In reality, there is a big difference: now, the
Power BI model is linked to the original source of
data, which is the SQL Server database. When
Power BI Desktop refreshes the information, it
does not need the Excel file (which if you recall
was manually updated by David and Karin).
Instead, by connecting directly to the database,
it always gathers the latest information available
at the moment of refresh. In other words, David
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eliminated Excel as a middle step, saving the
time and effort required to prepare that file.
Loading from multiple
sources
Working directly with a database looks great,
but, after further investigation, David
experienced an unpleasant surprise: by using
Excel, he was able to integrate into the same
table both the sales, which came from SQL
Server, and the budget forecasts, which came
from an Excel file. However, the Excel file
containing the forecast cannot be gathered from
the SQL Server database, because the
country/region managers update the Excel file
whenever they want to share some new figures
for 2016.
To solve this problem, we need to dive a bit
more into the internal structure of the Power BI
Desktop model. In Chapter 3, we said that a
Power BI Desktop model contains an internal
query, created by Power BI Desktop, for each
dataset. This internal query is not visible if you
perform basic operations, such as loading data
from an Excel file or from a SQL Server database.
Yet, it is there, and if you need to modify it, you
can.
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The query language of Power BI Desktop is used
by Query Editor, and discussion of that language
alone would fill an entire book of several
hundred pages. As you might imagine, we
cannot cover it in a mere few pages here.
Instead, we want to show you some basic
features of Query Editor so that you better
understand its capabilities.
More info If you are interested in learning
more, we suggest that you to read one of the
many good books about Query Editor. You can
find them by searching for the M language or
Power Query (Power Query was the previous
name of the Power BI Query Editor).
To modify a Query Editor script, on the Power BI
Desktop ribbon, on the Home tab, click Edit
Queries, as shown in Figure 4-5.
Figure 4-5: Click Edit Queries to access the Query
Editor window.
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Query Editor opens in a new window, presenting
myriad options, as depicted in Figure 4-6.
Figure 4-6: Power BI Desktop’s Query Editor is a
complete development environment in and of itself.
Let’s take a quick tour of the Query Editor
window. Along the top is the ribbon, which has
four tabs: Home, Transform, Add Column, and
View. Below the ribbon, on the left side, is the
Query pane, which displays a list of all the
queries for the model. The middle pane shows
the result of the query. The Query Settings pane
on the right displays the query properties.
In David’s scenario, he is already accessing the
2015 sales data from the Contoso database, so
the objective now is to create a new query that
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also retrieves the budget forecast information
from the Excel workbook.
To load data from a new dataset, on the ribbon,
in the New Query group of the Home tab, click
New Source, and then specify to load the data
from the Budget table. (The process is nearly
identical to what you already learned in Chapter
3.) This results in two tables in the Queries pane
on the left side of the Query Editor window, as
demonstrated in Figure 4-7.
Figure 4-7: Query Editor can create multiple datasets,
as you can see in this figure.
When you are finished editing, on the ribbon, on
the Home tab, click Close & Apply to load the
data into Power BI Desktop. When this is done,
the Power BI Desktop Fields pane shows the two
sources: the table in Excel, and the SQL Server
table in the Contoso database, as depicted in
Figure 4-8.
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Figure 4-8: The Fields pane lists all of the tables (and
columns) that the Power BI Desktop model is using.
Using Query Editor
At this point, David can build a report containing
both the Budget and Sales 2015 tables sliced by
the Brand column, for example. But, as shown in
Figure 4-9, a bad surprise is awaiting him: the
value of the budget is the same for all the
columns.
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Figure 4-9: In this chart, the value for Budget 2016 is
the same for all the columns, and it is too high.
The chart is confusing, at the very least. David
used the Brand and Sales2015 attributes from
the Sales2015 table, and the Budget 2016
column from the Budget table. Nevertheless, the
values shown for the budget are always the
same. Moreover, the value looks to be too high
for each brand.
The problem here is that if you use the Brand
column from the Sales2015 table, despite having
the same name, it is not the same as using the
Brand column from the Budget table. The two
columns have the same values and the same
name, but they are not the same column. In fact,
if you were to try replacing Brand in the chart
with the Brand column from the Budget table,
the result would be similar, but with the opposite
behavior. Figure 4-10 shows that by using the
Brand column from the Budget table, the budget
values are now correctly sliced, but the sales are
not.
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Figure 4-10: When slicing by Brand in Budget, the
behavior is the opposite: sales are not sliced, whereas
Budget is.
This would be a good time to digress and
discuss what the correct data model to represent
David’s dataset is. It would be useful, but
somewhat pedantic and not very relevant to the
objectives of this book. The important point here
is that you cannot slice numbers coming from
two tables using columns from only one of them,
unless the two tables share some kind of
relationship.
More info In this specific case, the two tables
have no relationships. Moreover, a relationship
cannot be created in an easy way: you would
need to use a third table—in the middle—that
can slice both of them. If you would like to
learn more about this, read our book, Data
Modeling with Excel 2016 and Power BI (2016,
O’Reilly Media).
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To solve the problem, you need to bring the
Budget 2016 column from the Excel Budget table
into the Sales2015 table, exactly where it was in
the original Excel file. Using the technical terms,
we say that you need to join the two tables
together, copying the Budget column for the
given country/region and brand. It turns out that
Power BI Desktop’s Query Editor is the perfect
tool to perform such an operation.
In fact, with Query Editor you can load tables in
an easy way, but, as we said earlier, you also
have the option of editing the autogenerated
query to make it behave differently. Let’s catch
up with David to see how he does this.
David goes back to the Power BI Desktop Query
Editor window and modifies the Sales 2015
query. He selects the Sales 2015 query and then,
on the ribbon, on the Home tab, he clicks Merge
Queries (see Figure 4-11).
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Figure 4-11: Click Merge Queries to join multiple
queries into a single one.
This opens the Merge dialog box, in which you
need to specify the destination table (the source
is the one selected) and which columns to use to
join them together. In David’s case, the columns
to use are CountryRegion and Brand, in both
tables, as illustrated in Figure 4-12.
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Figure 4-12: In the Merge dialog box, you can select
which columns to use when merging two tables.
When you are selecting the columns to merge,
Query Editor might display a dialog box similar
to that shown in Figure 4-13, asking you for the
privacy level of the data sources.
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Figure 4-13: Query Editor needs to know the privacy
levels of your data sources in order to merge them.
Privacy levels are used to ensure that you do not
send private information to data sources outside
of your secure area. Setting the wrong privacy
level might expose sensitive data to untrusted
sources or might affect the performance of the
query. In David’s case, he sets both sources to
Private because both sources are within its
network. If you are loading data from the web,
for example, you should mark that data source
as Public; this will avoid sending information to
the web that is coming from one private source.
More info A complete discussion of privacy
levels is beyond the scope of this book. If you
are interested in learning more about them, go
to http://aka.ms/privacylevelspowerquery.
The last option available in the Merge dialog box
is Join Kind. You use this to choose what
happens with rows in one table that have no
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corresponding rows in the other table. For
example, if there are sales for a country/region
but there is no budget for it, should that
country/region be included in the resulting
dataset? The most typical kind of join is the
default: a Left Outer join, which includes all the
rows from the source table and only matching
rows from the merged one. In other words, in
David’s case, it retrieves all the sales, plus the
budget for the countries/regions and brands
with sales.
Note In case there were new countries/regions
or new brands, David should have used a Full
Outer join so as to include both
countries/regions and brands with no sales but
with budget data, plus countries/regions and
brands with no budget but with sales data.
There are many kinds of joins, but the most
useful ones are left outer and full outer. The
remaining ones are somewhat exotic,
interesting only for very technical people.
After David clicks OK, the table shows a new
column named NewColumn, whose content is
from a table, as shown in Figure 4-14.
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Figure 4-14: When you merge two tables, the result is
the original column, plus a new column of type Table.
In fact, when you merge two tables, the result is
the original column, plus a new column of type
Table. This new column contains all of the rows
that are related with the current row in the
original table. In David’s case, the table contains
a single row, but, for more complex joins, it
might contain many rows.
David is not interested in retrieving the full table.
Instead, he wants only the Budget column. To do
this, he needs to expand the NewColumn table
so that it includes only the columns he wants. He
can do this easily by clicking the two arrows
adjacent to the column name, in the yellow box
around the column. This opens the expand
column dialog box, as illustrated in Figure 4-15.
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Figure 4-15: You can choose which columns to
include in the result dataset in the expand column
dialog box.
In the example, David selected only the Budget
2016 column. The result of this is that instead of
NewColumn, you now have the Budget 2016
column for each row of the Sales table, as
depicted in Figure 4-16.
Figure 4-16: When you expand a column, it is
replaced with the columns you chose.
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David is nearly done. The last step is that the
column, as it is, shows the full yearly budget,
whereas the Sales table should contain only the
monthly budget. In Excel, David divided the
budget value by 12; hence, he is doing the same
here. To perform this, on the ribbon, on the Add
Column tab, he clicks Add Custom Column (in
the General group) to create a new column
containing the value of Budget 2016 divided
by 12, as demonstrated in Figure 4-17.
Figure 4-17: You can add new calculated columns to
your query by using custom columns in Query Editor.
To create a new column, David must provide the
expression that computes it. He can do that in
the Add Custom Column dialog box that opens,
as shown in Figure 4-18.
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Figure 4-18: When you create a new column, you
enter the formula in the Custom Column Formula box.
Figure 4-19 presents the result of all these steps,
in which you can see the newly computed
Budget column.
Figure 4-19: The new Budget custom column
appears at the far right of the table.
As a final step, David right-clicks and removes
the Budget 2016 column, which is no longer
useful.
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Note You can delete columns in Query Editor
even if they are used in other calculations.
Unlike Excel, which saves formulas, Query Editor
saves the steps of a calculation, and it will run
them similarly to what you did by using the
user interface.
Before saving the query, David needs to perform
a final step: he defines the data type of the
column. By default, custom columns are of the
Any data type, meaning that the data type is not
defined. But, because he wants to use it to
aggregate values (which are, numbers), he must
change the data type to Decimal Number, as
shown in Figure 4-20.
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Figure 4-20: Changing the data type for the Budget
custom column.
When this work is done, David ends up with a
Sales2015 table that looks identical to that of its
Excel counterpart. The big difference now is that
the value of sales is computed from the SQL
Server database and, when the model is
refreshed, it will retrieve the latest figures in the
Sales2015 table, with no manual intervention.
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Hiding or removing
tables
There is a last, small issue with this model. Using
Query Editor, David moved the budget figures to
the Sales2015 table to create a single table with
all the columns required for his report. But, the
Fields pane continues to display the Budget
table. This might be confusing for Wendy and
other people looking at the report.
You can resolve the issue in either of two ways:
hide the Budget table from the Fields list, or
avoid loading it altogether. To hide a table, in
the Fields pane, right-click the table name and
then, on the shortcut menu that opens, click
Hide, as depicted in Figure 4-21.
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Figure 4-21: You can hide a table by right-clicking the
table name and then selecting Hide on the shortcut
menu.
A hidden table, as its name implies, is no longer
visible in the Fields pane. You can always make it
visible again by choosing View Hidden from the
context menu of any table of the field pane and
then clearing the Hide check mark. Keep in mind
that hiding a table does not mean it is at all
secure. A hidden table is only marked as not
visible, but any user can see it by simply using
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the user interface; hiding a table simply makes
the model easier to browse and less error-prone.
In David’s case, he would prefer to avoid loading
the table altogether. In fact, all of the
information needed to build the reports is now
stored in the Sales 2015 table. The Budget table
is used by Query Editor to merge the budget
(divided by 12) into Sales 2015. After the budget
information is stored in Sales 2015, the Budget
table is redundant and does not need to be
loaded.
To avoid loading a table, in Query Editor, in the
Queries pane, right-click the Budget query that
you want to remove and then, on the shortcut
menu, clear the check mark beside Enable Load,
as shown in Figure 4-22.
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Figure 4-22: You can turn on or turn off loading for a
table by using Query Editor.
When you turn off loading for a table, Query
Editor warns you with the message shown in
Figure 4-23.
Figure 4-23: Before removing a table, Query Editor
warns you about possible data loss.
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If you continue, the table will be removed from
the model, which becomes a single-table model
again, with Sales 2015 containing all the relevant
information.
Handling seasonality
and sorting months
Recall from Chapter 2 that Wendy had some
notes about seasonality. In fact, David splits the
budget figures by 12, but, in reality, many brands
show some seasonal effect that is not taken into
account while computing the budget. Moreover,
because some brands do not have sales at all in
some months, the final report does not contain
all the months.
For example, you can spot the problem easily by
looking at the budget data report for Wide
World Importers in China, as depicted in Figure
4-24.
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Figure 4-24: Some brands in China, for example,
have no sales in November and September.
There are two issues with the report in
Figure 4-24:


164
Sales for January and December are missing
from the tabular data. This is because there
are no sales in January and December, so the
corresponding rows are missing. This is a big
issue, because the budget is computed as
the total budget divided by 12, but only 10
rows are accounted for in the final figures.
As a result, the budget values of the reports
are wrong. In fact, the budget for World
Wide Importers in 2016 was 37,500, whereas
the report shows a total of only 31,250.
While searching for the missing months, you
might have noticed that months are not
sorted in sequential order. In fact, by default,
C H A P T E R 4 | Using Power BI Desktop
Power BI sorts each column alphabetically,
which, of course, is not the correct way to
sort months.
Note When you have a date column in the
data and you use it in Power BI Desktop, the
month name proposed is already sorted
alphabetically. However, in this case, the data
source contains the month name and not a
date, so you need to correct the sorting order.
David is determined to solve these two issues,
beginning with the last one, which is somewhat
easier. To sort the months by sequential order,
he needs a new column in the Sales table
containing a number, ranging from 1 to 12,
which contains the sort order of the month. The
problem is there is no such column available,
and there is no predefined functionality to
achieve this goal.
If David’s data were still in Excel, he could easily
add the column to the table manually, but now
data is coming from SQL Server, in the Contoso
database, and he cannot modify the content of
the SQL Server view to show such a month.
Fortunately, Power BI Desktop offers you a great
feature when you have some data to add to an
existing model: you simply enter it. To do this, on
the Query Editor ribbon, on the Home tab, in the
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New Query group, click Enter Data, and Query
Editor shows you a grid in the Create Table
dialog box, in which you can type (or paste) the
data you want to add to the model. Figure 4-25
shows how David used this feature to create a
table containing the month names and numbers.
Figure 4-25: Using the Enter Data functionality, you
can type or paste new datasets.
The next step, after saving and renaming the
table as Month Numbers, is to bring the Month
Number column from this table into Sales 2015.
The technique you use is very similar to what
David already did with the budget: join the Sales
2015 table with Month Numbers. This time, the
relationship is based on the month name.
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Figure 4-26 presents the Merge dialog box
already prepared.
Figure 4-26: Merging the Sales 2015 table with the
Month Numbers table.
After the merge, you still need to expand the
Month Number column and load the content in
the Power BI Desktop data model, similar to
what you did in back in Figure 4-15.
Now that the month number belongs to the
table, you need to instruct Power BI to sort the
month names by month number. In the Fields
pane, click the month name. Note that the
ribbon displays a new tab: Modeling. On the
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Modeling tab, click Sort By Column (number 1 in
Figure 4-27), and then select Month Number
(number 2 in Figure 4-27).
Figure 4-27: You can sort month names by numbers
by using the Sort By Column feature.
After you select it, the report changes and shows
the months correctly sorted, as demonstrated in
Figure 4-28.
Note It is a good practice to hide the column
that you use to sort another visible column.
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Figure 4-28: In this report, the months are now sorted
properly.
Now that David has the months displaying
correctly, it is much clearer that January and
December are missing, so he turns his attention
to fix this. Because this requires a bit more work,
there needs to be a bit of fore planning.
First, David needs to decide which year to use as
a basis for demonstrating seasonality. In fact,
Wide World Importers might have sales in
January 2014 and no sales in January 2015.
Should he consider 2014 or 2015 to decide what
to allocate in January? David goes for 2015
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because it shows the best figures. You might
make different decisions here, but as we have
cautioned several times already, keep in mind
that this is a book about Power BI, not a
budgeting tutorial. So, please be patient; we
are very naïve regarding choices like this one.
Now that David has made his decision, he
needs to build a table containing, for each
country/region and brand, the number of
months for which there are sales in 2015.
This requires several steps in Query Editor:
1. Start from Sales 2015, and then remove all
the unwanted columns, to keep only
CountryRegion, Brand, Month, and
Sales2015.
2. Remove all the rows in Sales 2015 that are
empty.
3. Remove the Sales2015 column.
4. For each brand, count the number of
months.
The first part of step 1 is easy: in Query Editor,
right-click Sales 2015, and then, on the shortcut
menu, click Duplicate to make a copy of the
table. Name the copy Months Count.
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The second part of step 1 is also easy: using the
small delete icon that appears in the applied
steps of the Query Settings panel (see
Figure 4-29), remove all of the steps, keeping
only the first two (Source and Navigation), so
as to return to the original query.
Figure 4-29: Use the small delete icon to remove
unwanted steps.
Remember, we are working on a copy of Sales
2015, so we are free to update it as needed. The
original table remains untouched.
At this point, you can delete the two Sales2013
and Sales2014 columns, which are not needed
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click the column header for each column and
then, on the shortcut menu, click Remove
Columns. Step 1 is done!
Moving on to step 2: David notices that the first
row (China, Adventure Works, July) contains a
null value for Sales2015. He right-clicks the value
to open its shortcut menu, where he chooses
Number Filters and then Does Not Equal,
meaning that he wants to filter only the rows
for which Sales2015 is not null, as shown in
Figure 4-30.
Figure 4-30: Right-click a cell value to filter rows
using several criteria.
Step 3: At this point, the column Sales2015 is no
longer useful; David can remove it as he did with
the other two years. Steps 2 and 3 are now also
done, in just a few clicks.
Finally, step 4: Group the current dataset by
CountryRegion and Brand, then count, for each
combination, the number of months. Because
this is a very common operation on datasets,
Query Editor offers a specific functionality: first,
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select the columns to group by, and then choose
Group By, as illustrated in Figure 4-31.
Figure 4-31: First, select the columns to group by,
and then click Group By to open the Group By dialog
box.
You use the Group By dialog box to choose the
columns to group and the operation to perform
on other columns. In this case, the default
options are good (see Figure 4-32): David wants
to group by CountryRegion and Brand, and then
count the number of rows (which is, months) for
each combination.
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Figure 4-32: In the Group By dialog box, you choose
the grouping parameters.
After he clicks OK to confirm this dialog box,
David sees the dataset shown in Figure 4-33.
Figure 4-33: The final dataset contains the columns
CountryRegion, Brand, and Number Of Months.
Now, the new dataset indicates how many
months are present for each brand and
country/region. You need to use this number,
instead of 12, in the division of the budget to
obtain the correct value to use for each month.
Of course, you do not want this table in the
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model, so you turn off loading for it. This table is
considered a helper table: you will use it with a
join operation with Sales 2015. The table
contains information that is useful only during
the join operation, but not beyond that.
Thus, the last step is to modify Sales 2015 to
take this number into account. This time, you do
not need to add further steps to an existing
query; however, you need to replace some of
them, and this requires a bit more attention.
If you reopen the Sales 2015 query in Query
Editor and begin navigating through the Applied
Steps panel, you notice that what is displayed in
the results pane reflects what the query looks
like after having applied the selected step. For
example, in Figure 4-34 you can see that when
you select the Added Custom step, the query
shows the Budget 2016 column, which will be
removed by the next step.
Figure 4-34: Navigating through the Applied Steps
area of the Query Settings pane, you can view partial
results of the final query.
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You need to add a few steps before the Added
Custom step (which computes the Budget,
divided by 12) and then modify the calculation of
the budget itself. When you choose an operation
from the toolbar, the step is added right after
the currently selected one. Thus, you select the
fourth step (Expanded NewColumn) and, there,
you add the merge of Sales 2015 with Months
Count, basing the relationship on CountryRegion
and Brand.
Note When you insert a step into a query,
Query Editor warns you about possible issues
with the query. In this case, we do not have to
worry, because we are not modifying the query
behavior; we are only adding a new column
that is coming from another query. There are
scenarios, however, for which this warning
makes sense. If you remove or rename a
column that is used later, the query might
break because of your changes.
Figure 4-35 presents the Merge dialog box for
the inclusion of the Number Of Months column.
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Figure 4-35: The parameters to bring the Number Of
Months column into Sales 2015.
After you add the column to the view, you must
replace the expression for the Budget 2016
column with a different one. In fact, you want to
compute the Budget 2016 column divided by the
number of months, for only the months for
which there are sales in 2015. To perform this
operation, in the Applied Steps area of the Query
Settings pane, click the settings button (the small
“gear” icon) to the right of the Added Custom
step, and then change the expression
accordingly, so that it appears like that
shown in Figure 4-36.
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Figure 4-36: The new expression for the Budget
column tests Sales2015 and divides Budget 2016 by
Number Of Months.
With the new query in place, the report is
updated as soon as you click OK, and now it
shows correct figures, as demonstrated in
Figure 4-37.
Figure 4-37: The report now shows the correct value
of 37,500.00 for the budget.
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Conclusions
In this chapter, you learned the basics of Power
BI Desktop, which is a desktop application that
brings the full strength of Power BI to your PC.
This was a basic introduction, and we will explore
more features later in this book. In fact, there is a
lot more to learn about Power BI Desktop, but
that would be beyond the scope of this book.
Here are the most relevant features:




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Power BI Desktop can load data from any
database. In the example in this chapter, we
used Microsoft SQL Server.
Using Power BI Desktop, you can load data
from multiple sources. In the example, we
mixed data from Excel with data residing in a
SQL Server database.
Power BI Desktop uses Query Editor to load
data. Query Editor offers many powerful
features. We highlighted in particular the
capability of merging different queries and
adding calculations to the query.
Some queries are loaded into the model;
others are useful only to compute values in
the main query. You can mark queries that
C H A P T E R 4 | Using Power BI Desktop
should not go into the model as “do not
load” so that they are used only in Query
Editor.

You can upload a Power BI Desktop model
to the Power BI online service, and it retains
the same refresh features: by using the
Personal Gateway, you can refresh a Power
BI model in the cloud, letting it access data
on your PC.
At the beginning, it might look complex, but
after you get used to it, Query Editor offers you a
lot of power to build your models. Having
successfully finished this chapter, you can call
yourself a data modeler. Later in the book, we
will introduce some more features of Power BI
Desktop to further enhance your model with
more advanced calculations.
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Now that
you’ve
read the
book...
Tell us what you think!
Was it useful?
Did it teach you what you wanted to learn?
Was there room for improvement?
Let us know at http://aka.ms/tellpress
Your feedback goes directly to the staff at
Microsoft Press, and we read every one of
your responses. Thanks in advance!
CHAPTER
5
Getting data from
services and
content packs
Working on the forecast for the
next year, David realizes that it
would be useful to consider the
statistics for the pages visited on
the company website. Using these
insights, he might anticipate which
products will gain more traction in
the upcoming months.
Microsoft Power BI provides many
connectors and content packs that
make it possible for David to easily
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content packs
access the data generated from
different cloud services. Content
packs are also a useful tool to
deploy and share predefined
models and reports within a
company.
In this chapter, you will see how David imports
data from Google Analytics into Power BI, using
different techniques. You can use these same
techniques to access many web services, the list
of which is growing rapidly, week by week.
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Note In the following examples in this
chapter, we will show data gathered by
using Google Analytics from the website
www.daxformatter.com, which we created to
help users format DAX expressions and queries.
We will use this data as part of the scenario we
create for David. Our assumption is that the
data would make sense in the sales generated
by Contoso, too. But, also, we would not have
enough data to show if we created a fictitious
website for these examples. For this reason,
when you continue reading, assume that
visitors from this website are meaningful for
sales of Contoso products, even if we know this
is not true. If you want to replicate the same
example, you can use Google Analytics data for
a website to which you have access.
Consuming a service
content pack
David wants to analyze data regarding customer
visits to the website in an effort to glean some
early indicators of a potential growth in certain
countries/regions. If the number of visitors to the
website increases in certain countries/regions,
there also could be a growth in sales for the
same country/region. Looking at this information
for the previous two years might help in the
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budgeting process by providing data that will
help David define the sales target for each
country/region. Comparing website visitors and
sales by origin in the same report is an important
step in the analysis that he wants to accomplish.
David knows that his company’s website is
monitored by Google Analytics, and he wonders
whether Power BI supports it. Reading the Power
BI documentation, he finds that Google Analytics
is indeed supported as a service content pack in
the Power BI service, and as a connector in
Power BI Desktop. With that, he begins using the
service content pack in the Power BI service.
David starts Power BI and then, in the lower left
corner of the window, he clicks the Get button.
He is then greeted by the Get Data page, as
depicted in Figure 5-1.
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Figure 5-1: The choices available to you to get data in
Power BI.
When you load data from a service, Power BI
automatically creates for you a data source, a
report, and a dashboard connected to that
service, using predefined templates for each of
these elements. You can modify these objects
later if you want. Obviously, Power BI will use
your credentials to access the service. Thus, even
if the report is the same, the numbers will
represent your own data.
What is a content pack?
A content pack can include the following parts,
which have dependencies among them:
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Dataset One or more datasets provide data
to reports and dashboards included in the
content pack library.
Report Each report in a content pack
connects to a dataset included in the same
content pack. The datasets required by reports
are always part of the pack.
Dashboard Each dashboard of a content pack
includes visualizations of one or more reports
included in the same content pack. The reports
used by a dashboard are always part of the
pack.
David wants to use a content pack for a service;
so, in the Content Pack Library section of the Get
Data page, on the Services tile, he clicks the Get
button. This displays a list of the services that are
available, as shown in Figure 5-2.
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Figure 5-2: A partial list of the services that are
available in Content Pack Library.
He clicks the Google Analytics tile and sees a
message containing a description of the service,
as illustrated in Figure 5-3.
Figure 5-3: The description of the selected Google
Analytics content pack.
Note For all available services, Power BI
presents a similar description along with a
Connect button. However, the steps that follow
after you click Connect might differ from those
for Google Analytics, depending on the security
model and the implementation of the
authentication of the user for the selected
process. So, keep in mind that if you select
another service, the experience might be
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altogether different from what we describe in
this chapter.
David clicks Connect and is then prompted to
choose the authentication method to use to
connect to Google Analytics. The only choice
available to him is oAuth2, as depicted in
Figure 5-4.
Figure 5-4: You must choose an authentication
method when connecting to Google Analytics.
Be aware that after you click Sign In, you might
be requested to sign in to Google Analytics (see
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Figure 5-5), unless your credentials are already
stored because of previous access.
Figure 5-5: The Google sign-in page.
When David completes the sign-in, he is asked
to provide offline access to the Power BI
application, as shown in Figure 5-6. To authorize
Power BI to retrieve data from the Google
Analytics service on his behalf, he clicks Allow.
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Figure 5-6: Confirming offline access to Google
Analytics for Power BI.
Note By allowing “offline access” you are
indicating that you want to allow the Power BI
Desktop application to interact with Google
Analytics even when you are not interacting
with the Google Analytics service directly. For
example, when you navigate in the data offered
by Google Analytics in a web browser, you are
engaging in an “online access,” because you
directly interact with the service. However,
when Power BI Desktop requests data from
Google Analytics, it will act on your behalf, and
you will not see the details of every request.
This is true not only when you use Power BI
Desktop interactively, but also whenever you
schedule an automatic refresh of the dataset in
Power BI.
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On the next page of the Connect To Google
Analytics dialog box, David can choose which
part of the data available in Google Analytics
that he wants to bring into the content pack.
There are three options (see Figure 5-7):



191
Account The account name for Google
Analytics (a single user might have access
rights to multiple accounts). Select from the
list of available account names for the
current user.
Property The name of the property (which
is a Google Analytics concept) within the
data owned by the selected account.
View The view name within the property.
Oftentimes, the view corresponds to the
property, unless you handle multiple
websites within the same account.
C H A P T E R 5 | Getting data from services and
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Figure 5-7: Choosing what part of the data to import
from Google Analytics.
Note In our example, for the DAX Formatter
property, the View option is unavailable
because there is only one view from which to
choose.
When David clicks Import, the Power BI service
copies the content library into the currently
selected workspace, and then it updates the
connection with the information provided in the
previous steps. Finally, it populates the
workspace with data read from the Google
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Analytics service. As a result, he obtains one
dashboard, one report, and one dataset named
Google Analytics, which he can rename if
necessary (you should do that in case you import
multiple copies of the same content pack in the
same workspace—in this case, they would look
the same, but they would contain different data).
The Google Analytics dashboard includes
information about the traffic received in the past
30 days, as demonstrated in Figure 5-8.
Figure 5-8: A dashboard created by the Google
Analytics content pack.
Let’s take a moment to discuss this dashboard. If
you click one of the dashboard’s visualizations,
you are moved to the underlying data in a
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corresponding report. All of the visualizations
come from the same report, which has pages
that filter the data from the past 30 days (such as
Total Users), the past 90 days (such as Site
Traffic), or the past 180 days (such as System
Usage, Page Performance, and Top Pages).
Figure 5-9 presents the System Usage page for
David’s report.
Figure 5-9: A report created by the Google Analytics
content pack.
Back to David; he can now edit the report
created by the content pack, or he can create a
new report based on the same dataset used by
the existing report. In both cases, one of the
issues is that the dataset provided in this content
pack does not include all the possible measures
and slicers available in the Google Analytics
server. Also the available historical depth is
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limited to a maximum of 180 days. Figure 5-10
demonstrates that the number of tables,
attributes, and measures available is just a
fraction of the measures available in Google
Analytics.
Figure 5-10: The tables, attributes, and measures
available in the dataset of the Google Analytics
content pack.
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The reason for this is that the engineers who
created this content pack tried to include the
minimum amount of information required to
create the desired reports. In this way, the size of
the resulting Power BI file (.pbix) is kept to a
minimum, improving the performance of many
related operations. However, the level of details
available might not satisfy your requirements,
depending on what you are trying to accomplish.
The Google Analytics content pack has been
useful for an initial overview of the data
available, but it does not meet David’s
requirements. First, he needs greater historical
depth to analyze the trends, and the limit of 180
days provided in the content pack is not
sufficient for his requirements. Second, he would
like to create a single report showing the
relationship between data from Google Analytics
and other data, such as past sales and forecast.
This requires a single dataset with multiple
connections, so getting data from the Google
Analytics content pack is not very helpful,
because you cannot change or customize the
data model of a dataset copied from a content
pack, whereas you can customize the reports and
dashboard imported from a content pack.
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For these reasons, David will create a new
dataset using only the connection to Google
Analytics in Power BI Desktop, without relying on
the content pack he has used thus far.
Creating a custom
dataset from a service
David wants to show in the same report some
measures from Google Analytics related only to
China, Germany, and the United States, which
are the countries/regions interested in the
budget process. He wants a better result than
the one he can obtain by using the Google
Analytics content pack on Power BI. Also, he
needs greater historical depth than what is
available through the service he tried earlier.
Instead of using the predefined (and read-only)
content pack, David will use Power BI Desktop to
connect to Google Analytics, and then include
the Google dataset in the budget model he is
developing. Of course, this requires some more
effort, but it provides him with the advantage of
flexibility in the definition of measures,
calculations, new tables, and relationships in the
data model.
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Thus, David begins with the model he created by
using Power BI Desktop in Chapter 4, which, to
refresh your memory, has one table that contains
the sales and budget in different columns. Such
a table will be useful to analyze historical trends
in sales. However, to import other tables from
Google Analytics, he needs to use a special
connector that imports data from Google
Analytics directly into the model of Power BI
Desktop.
David starts Power BI Desktop. On the ribbon, on
the Home tab, he clicks Get Data and then
selects the More option. In the Get Data dialog
box that opens, in the pane on the left, he clicks
the Other category, and then, in the pane on the
right, he clicks Google Analytics, as shown in
Figure 5-11.
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Figure 5-11: The Google Analytics connector is
available in the Get Data dialog box.
When he clicks the Connect button, he is asked
to sign in to his Google Account (Figure 5-12) so
that Power BI Desktop will be able to access
information in Google Analytics to which his
Google Account has permission.
Note You might see the Connecting To A
Third-Party Service message box, warning you
that the features, updates, and availability
change often. Click Continue to close the
message and move on to the next step.
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When you sign in for the first time, you must
provide your user name and password. (The
sign-in process might use 2-factor
authentication if required.) After you complete
the sign-in process, you can click the Connect
button to move forward.
Figure 5-12: Connecting to Google Analytics from
Power BI Desktop requires that you sign in to a
related Google Account.
When David completes the sign-in process, he
then needs to select the attributes and the
measures to import in the data model. The
Navigator dialog box provides a list of all the
services monitored by the Google Account that
he is using. After he selects the service, he is
presented with a list of folders containing
attributes and measures that he can select and
import. The attributes are information collected
by Google Analytics, such as date-related
columns (year, month, day, etc.), demographic
and geographical information about the visitors
(such as age, gender, country/region, and city),
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and many other pieces of information that can
be used to group and filter data. The measures
are numeric information describing the
frequency or the size of an event; for example,
the number of users, the number of visits, the
average time to load a page, and so on.
Querying a service such as Google Analytics is
similar to querying a data model by using a pivot
table. You select certain attributes, and measures
are automatically aggregated at the granularity
defined by the attributes included within the
same report. For example, Figure 5-13 depicts
the preview of the result obtained by selecting
the Country/Region attribute from the Geo
Network folder, the Year attribute from the Time
folder, the Sessions measure from the Session
folder, and the New Users and Users measures
from the User folder.
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Figure 5-13: Connecting to Google Analytics, you
select the measures and the attributes to import in the
data model.
Choosing the right granularity
The approach for getting data from a service
such as Google Analytics is different from the
one you use when you collect data from a
relational database (such as SQL Server) or
from an external Excel file. In these cases, you
always see the data at the maximum level of
detail (also known as “fine granularity”). The
selection of the attributes defines the
granularity, and you can still change the query
later in Query Editor, but you must add other
attributes to the query in order to increase the
granularity. You have a similar user interface in
Query Editor when you import data from an
external rich semantic model, such as the one
provided by Analysis Services and SAP HANA.
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In Power BI Desktop, on the Home tab, David
clicks Edit Queries to open the Query Editor
window. Because David wants to analyze a
limited number of countries/regions, he needs to
apply a filter in Query Editor to the
Country/Region column, restricting the selection
to China, Germany, and the United States. This
obtains the result illustrated in Figure 5-14.
Figure 5-14: Using Query Editor to refine the query
for getting data from Google Analytics.
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Also note in Figure 5-14 that the ribbon includes
a special tab: Cube Tools | Manage. The name
“cube” references an external rich semantic
model, and as we said earlier, it is the same
approach available for Analysis Services and SAP
Hana. When you click Add Items, you can select
additional attributes and measures that will
modify the query made to Google Analytics,
enhancing the granularity (with attributes) or
more information (with measures). Figure 5-15
depicts the different graphical representation of
attributes (in the Audience folder) and measures
(in the DoubleClick Campaign Manager folder).
Note You should use the Collapse Columns
button whenever you want to remove an
attribute and obtain a corresponding
granularity without the attribute you removed.
If you just remove an attribute by removing the
corresponding column in Query Editor, you do
not modify the original query, and the
cardinality will still include the attribute you
removed. A detailed tutorial about how to use
Query Editor with each data source is beyond
the scope of this book, but you should be
aware of this important difference compared to
other types of data sources.
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Figure 5-15: The Add Items dialog box in which you
can add measures and attributes from a Google
Analytics connection.
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After David confirms the items to import, Power
BI Desktop sends the query to Google Analytics
and imports the result in a new table, as shown
in Figure 5-16, in which you can see the content
of the table named Website, containing the
results of the query to Google Analytics defined
so far.
Figure 5-16: The table Website is the result of the
query to Google Analytics.
Note For the purposes of this scenario, our
intrepid budgeting manager, David, does not
create relationships between the table with
data from Google Analytics and the other table
in the same data model. However, it is common
to create relationships between tables in order
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to simplify data navigation. You will see more
complex data models in Chapter 6.
The key metric that David wants to obtain is the
growth in new users that each country/region
experienced in 2015. The data available in the
Google Analytics service content pack did not
have the historical depth required for this
analysis; David requires data for both 2014 and
2015. The data imported in Power BI Desktop
does not have such a limitation, so it is possible
to import such historical depth. However, to
create the calculation of the growth percentage,
David has to learn the language used by Power
BI, which is called DAX.
DAX, which stands for Data Analysis Expressions,
was introduced in Power Pivot for Excel in 2010
and is based on the Excel formula language. If
you have experience with Excel, you will find
many functions that have the same name and
syntax as those in your favorite spreadsheet. But,
there are also several new concepts and
functions that would require a separate book to
cover fully. Fortunately, these books exist, such
as The Definitive Guide to DAX, published by
Microsoft Press. You will find a very basic
discussion about DAX “measures” (the DAX term
used to refer to scripts) in Chapter 6 of this book.
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Because David used Power Pivot for Excel in the
past, he already knows how to write the measure
he needs. So, on the ribbon, on the Home tab,
he clicks New Measure and inserts the following
DAX measure in the formula bar:
New Users Growth =
IF (
HASONEVALUE ( Website[Year] ),
DIVIDE (
SUM ( Website[New Users] ),
CALCULATE (
SUM ( Website[New Users] ),
Website[Year] = VALUES ( Website[Year]
) - 1
)
)
)
Then, he displays this measure in a separate
visualization, under the New Users metric,
grouped by country/region and year, as
illustrated in Figure 5-17.
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Figure 5-17: Two visualizations displaying the New
Users measure and its growth, using data from
Google Analytics.
At this point, David has the data he needed from
Google Analytics, and he can consolidate that in
a single report, together with data coming from
other data sources. Chapter 4 shows you how to
load data from different data sources, and
Chapter 6 shows you how you can combine this
data in a single model by using the DAX
language. This helps to improve the browsing
experience by providing a unique filter for each
entity (such as the Country/Region in this report)
instead of having similar columns in different
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tables that filter only the specific table to which
they belong.
David can now publish the report he created in
Power BI Desktop by using Power BI. By doing
that, he is able to pin report content to a
dashboard. Figure 5-18 depicts a dashboard
built by pinning the two visualizations of the
report he created. At this point, David has a
dashboard and a report published on Power BI
that are based on a custom dataset he created in
Power BI Desktop, which gets a particular
selection of data from Google Analytics.
Figure 5-18: A dashboard that includes two
visualizations displaying data from Google Analytics.
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Creating a content pack
for your organization
After David created a report in Power BI Desktop
using the Google Analytics connector, he wants
to share the result of his work with other
colleagues in the company, and he wonders
what the best tool is in Power BI to do that.
David realizes that the report he created would
be a good starting point for deeper insights
created by his colleagues. By using the share
feature, he is able to share only a dashboard and
its underlying reports, but he would like to
publish the report based on Google Analytics as
a template for reports created by other
colleagues. The sharing options you have seen
so far do not satisfy David’s requirements. He
wants other users to be able to customize the
reports he made and create new reports based
on his work. Sharing a dashboard does not
provide such flexibility. Using the group
workspace, there would be a single copy of
reports and dashboards shared among the
group’s users, which would not be visible to
users who are external to the group. However,
David wants to share the results of analysis
based on Google Analytics with other users
outside the budgeting group. Thus, sharing a
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dashboard and creating a group workspace are
not viable options.
The content pack for an organization is a good
solution for David’s requirements. This content
pack can contain datasets, reports, and
dashboards. Users receive a copy of these
objects that are automatically synchronized in
case a new version of the same content pack is
published. If users customize one of these
objects, they will work on their own copy of the
reports, which will no longer be synchronized
with the original one.
To create a content pack, in the upper-right
corner, click the Settings button (the small gear
icon), and then, on the menu that opens, select
Create Content Pack, as shown in Figure 5-19.
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Figure 5-19: The Create Content Pack command on
the Settings menu.
The Create Content Pack dialog box opens (see
Figure 5-20). Here, David provides data about
the new content pack. He can choose whether
the content pack should be visible to any user in
the organization or just to users belonging to
specific groups. In this case, David selects the My
Entire Organization option. He defines a title for
the content pack (Sales And Website 2015, in
this example) and a description of its content.
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It is important to provide a clear description
because this is what other users will read before
importing a content pack in their own
workspace. It is also possible to customize the
content pack by using a specific image, which
could be just the company logo or a more
customized graphic. Selecting an image is
optional; if you do not do that, a default one will
be used, instead.
The more important part of the Create Content
Pack dialog box is the area where you select the
item to publish. There are three lists for of all the
dashboards, reports, and the datasets you have
in your personal workspace. You can select any
number of the available entities, even if there are
a few constraints. If you select a dashboard, all of
the reports and datasets used in the
visualizations of that dashboard will be
automatically included, too. For example, in
Figure 5-20 you can see that the dashboard
Sales And Website is selected, and because of
that, the report and the dataset named Sales
And Website 2015 are automatically selected, as
well. You cannot remove the selection of a
dataset or a report if it is used in a dashboard.
The same is true when you select a report: the
underlying dataset is automatically selected, too.
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Figure 5-20: The Create Content Pack dialog box
requires you to select objects to publish in a new
content pack.
When you click the Publish button, the content
pack is published and displayed in the list of the
content packs that you can obtain by selecting
the View Content Pack item in the Settings menu
(refer to Figure 5-19).
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Figure 5-21 presents David’s list of content
packs.
Figure 5-21: List of content packs published by the
current user.
From this list, David can edit or delete each
content pack. If he were to select Edit, he would
return to the same configuration window shown
in Figure 5-20; however, this time the window
would be titled Update Content Pack instead of
Create Content Pack.
At this point, other users in his organization are
able to consume the content pack that David
created. In the next section, you will see how this
works and what the difference is between
consuming an existing content pack as is and
creating a personal copy that can be modified.
Consuming an
organizational content
pack
In this section, you will see how Wendy can use
the content pack that David created.
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When she clicks Get Data, Power BI opens the
familiar Get Data page, shown in Figure 5-22.
Figure 5-22: The Get Data page in Power BI.
In the Content Pack Library section in Figure 522, Wendy clicks Get on the My Organization
tile. This opens the list of content packs she can
use, along with a tile to create a new content
pack, as demonstrated in Figure 5-23. In this
scenario, the only content pack available to
Wendy is the one David published, Sales And
Website 2015.
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content packs
Figure 5-23: The list of content packs available in My
Organization.
Wendy clicks the Sales And Website 2015 tile
and then sees the information that David
previously included when he published the
content pack (refer back to Figure 5-20). This
information includes a description of the content
pack, the name of its publisher, and the time
since it was last published, as shown in Figure 524.
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content packs
Figure 5-24: Details about the selected content pack.
When Wendy clicks the Connect button, Power
BI imports into her personal workspace the
entities included in the content pack. She will
find a new dashboard (Sales And Website) has
been added to the left pane, as well as a new
report and a new dataset (both named Sales And
Website 2015), as illustrated in Figure 5-25.
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Figure 5-25: Entities included in the content pack that
are imported into a personal workspace.
Wendy can navigate in the dashboards and in
the reports in view mode without any issue. If
the content pack were to be updated by David,
the new version of datasets, reports, and
dashboards included in the content pack would
automatically replace those available to Wendy.
However, if She tries to pin something more in
the Sales And Website dashboard, or edit the
report Sales And Website 2015, or if she clicks on
the Sales and Website 2015 dataset to create a
new report based on such a dataset, a message
will appear (Figure 5-26), asking if she wants to
personalize the content pack.
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content packs
Figure 5-26: The request to personalize a content
pack.
The same option is also available as the
Personalize button when she clicks the option of
a dashboard, report, or dataset that she obtained
from a content pack, as demonstrated in Figure
5-27. These options also include the ability to
remove or to open the object.
Figure 5-27: The options available in a dashboard
obtained from a content pack.
If Wendy removes any object belonging to a
content pack, all of the other entities from the
same content pack are removed, as well. If she
proceeds with the personalization and clicks
Save in response to the message shown in Figure
5-26, she creates a copy of the objects in the
content pack, and she is able to modify or delete
them as she pleases.
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content packs
In other words, if Wendy does not create a
personal copy of the content pack, she will
automatically receive any update to that same
content pack that David publishes. If she creates
a personal copy, any future updates by David will
not propagate to her workspace. She will be able
to modify the dashboards and the reports in her
personal copy of the content pack; however, she
will not be able to modify the dataset, because a
dataset obtained from a content pack is always a
shared dataset, and only the package owner can
modify and refresh its content.
To recap, we have this following possible
behavior for each object type:


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Dashboards and Reports These can be
shared from the content pack, or they can be
copied from the content pack in a personal
copy. All of the dashboards and reports of a
content pack are copied to the personal
workspace if the user wants a personal copy
of the content pack, but those dashboards
and reports no longer receive updates from
the content pack publisher.
Datasets These are always owned by the
content pack publisher, who is the only
person who can schedule refresh operations
and change other definitions in the dataset.
C H A P T E R 5 | Getting data from services and
content packs
Updating an
organizational content
pack
David is the owner of the content pack named
Sales And Website 2015. The moment he
publishes the content pack on the Power BI
service, any ensuing changes to any object
included in the content pack on the Power BI
service will generate a special notification. For
example, if David changes the dashboard by
moving the visualizations to different positions,
he receives the warning message depicted in
Figure 5-28.
Figure 5-28: A warning message after changes are
made to an object included in a published content
pack.
Note If you edit and then publish a content
pack for a data model created with Power BI
Desktop, subsequent changes made to the
local data model (the .pbix file) do not
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content packs
automatically propagate to the Power BI service
and to the published content pack. You do not
receive any update in Power BI Desktop. It is up
to you to remember that you must publish the
.pbix file on the Power BI service to update the
data model in the cloud.
If David clicks View Content Packs, in the list of
the content packs he published, he sees which
ones are affected by one or more changes he
made to the reports and dashboards. Figure 529 shows that the only content pack he
published, named Sales And Website 2015,
displays a warning icon next to the name. When
David points to the icon, he can see the content
of the warning message, specifying that the
content pack must be updated in order to show
the changes to other users.
Figure 5-29: A content pack is marked with a warning
icon when it includes unpublished changes.
In practice, any changes made to objects that are
a part of a content pack are not automatically
published in a new version of the content pack
until the owner specifically performs such an
update. When David clicks the Edit action, he can
publish a new version of the content pack, which
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content packs
will include the current version of the objects
(dashboards, reports, and datasets), replacing
the version previously published with the same
name. Figure 5-30 shows that the Update
Content Pack dialog box is identical to that of
Create Content Pack, the only difference being
that the Publish button is now labeled Update.
Figure 5-30: The Update Content Pack dialog box is
the same as the Create Content Pack dialog box,
except the Publish button is now labeled Update.
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When David clicks Update, he generates a new
version of the content pack that overrides the
previous one. This action automatically updates
all of the objects within the content pack in all
the workspaces of any users who have
consumed the same content pack without any
customizations. This type of consumption of a
content pack corresponds to the share feature of
a dashboard, which provides a read-only copy of
the dashboard that other users can see but not
modify. However, using the content pack, this
capability is extended to reports and datasets,
which can be a part of a content pack regardless
of whether they’re used in a dashboard.
If a user who consumed the content pack
created her own copy of it, a warning message
will display in her workspace, notifying her that a
new version of the content pack is available, but
that is all that will happen, because changes in
the consumed content pack are no longer
automatically published to the corresponding
entities in her content pack. For example, earlier
we saw that Wendy created her own copy of the
content pack, so when David publishes the new
version of the content pack, she does not see
any changes applied to the Sales And Website
dashboard. However, because she still uses some
objects that were originally created from a
content pack that now has been updated, she
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receives the message shown in Figure 5-31 in
her Power BI window.
Figure 5-31: The warning message when there is an
update to a content pack that has been copied to a
personal copy.
Knowing that a new version of the content pack
is available, Wendy can decide whether to get
data from the same content pack again. If she
decides to do so, a new copy of all the objects
will be imported into her workspace. Because
these objects will have the same name as the
ones she previously copied, it would be a good
idea to rename objects imported from a content
pack when you decide to create your own
personal copy; this way, you would not confuse
them with the original copy in case you import
the same content pack again in the future.
Conclusions
In this chapter, you learned how to consume and
create content packs in Power BI. There are
different types of content packs, each one with
different behaviors available to the user.
Moreover, you have also seen that you can
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content packs
create custom datasets from a service when the
corresponding service content pack does not
provide the data model that you need.
Here are the most important features you
learned:




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A content pack contains a set of dashboards,
reports, and datasets that a Power BI user
can quickly import into his own personal
workspace. He can also customize
dashboards and reports imported from a
content pack, but not a dataset.
Content packs are available in the Power BI
service only.
A service content pack is published by
Microsoft that you can use to connect to a
service importing data that populates a set
of predefined dashboards and reports. You
must provide the credentials required to
connect to the service from which you want
to extract data.
You can create a custom dataset in Power BI
Desktop only, using a connector
corresponding to the service content pack
that you cannot customize. Be aware that
not all service packs have a corresponding
connector in Power BI Desktop, but there are
C H A P T E R 5 | Getting data from services and
content packs
several (such as Google Analytics) that have
both.



Any user can publish an organizational
content pack. These contain predefined
connections to data sources that cannot be
changed by the user who consumes a
content pack; only the content pack
publisher can modify those connections.
A user can consume an organizational
content pack by just reading its content or
by creating a personal copy that she then
can modify.
An organizational content pack publisher
can update data. Such changes are
automatically propagated to users who
consume the content pack in a read-only
mode.
Content packs are an important tool to quickly
create a set of predefined reports and
dashboards based on data coming from an
existing external service, or from a dataset
created within the organization.
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CHAPTER
6
Building a data
model
In this chapter, David moves to the
next level in Microsoft Power BI
usage. We are probably cheating
a bit now, but we wanted this book
to show you what Power BI can do
for you when you master it, not
just demonstrate its basic features.
To do that, we presume that
David—encouraged by the good
results so far—spent some time
learning the basics of data
modeling and the DAX language.
Having learned more details about
Power BI, he begins again building
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the budgeting solution, but this
time he can trust his better
knowledge of the tools.
David loads the sales in the previous years, but
unlike the last time, he does not use the view
that Karin, the database administrator at
Contoso, created for him. Instead, he uses more
basic views, on top of the Contoso data
warehouse, that provide data in a more
fragmented way. There is a table containing
information on the stores, one with the sales
data, one for the date, and, lastly, a table of the
products themselves. Using this information, he
builds a first sales analysis project. Finally, he
adds the budget information from the Microsoft
Excel workbook and writes some DAX code to
prepare the dashboards.
We will not discuss in detail all of the formulas
and intricacies of the code and the data model;
it’s not realistic to expect you to learn how to
perform these operations by reading one
chapter. Our goal is to build the full project
together (remember, you can replicate it by
using the companion content). If you like the
final project, you will probably be better
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motivated to proceed further with your study
and follow David’s path in learning data
modeling and DAX.
Loading individual
tables
Recall from Chapter 3 that David needed to
speak with Karin to gain access to the Microsoft
SQL Server database containing a view that
returns sales for the past three years. David
learned that he can perform an analysis on sales
in a better way if—instead of using Karin’s
view—he loads the data from the original tables
where Karin stores Contoso information. So, he
arranges a meeting with Karin to gather more
information about the internal structure of the
Contoso data warehouse.
Karin explains to him that the database is
organized in tables that he can access by using
individual views (one per table). There is a table
for each business entity of Contoso’s business:

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Products This table contains information
about the products sold by Contoso.
C H A P T E R 6 | Building a data model



Sales This one contains detailed sales, one
row for each individual sale.
Stores This table has information about
the stores where the sales were transacted.
Date This is a helper table that contains
the calendar. David learned in a Business
Intelligence class that such a table is of
paramount importance when building a
good data model.
Karin gives David access to the views so that he
can load the granular information. David decides
to begin again from scratch, so he opens Power
BI Desktop and loads these tables into a new
model, following the same procedure he did to
load the Sales2015 view. The only difference is
this time he loads four tables at once, as shown
in Figure 6-1.
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Figure 6-1: Using the Navigator dialog box, you can
load multiple tables at once.
Instead of loading the tables directly from the
Navigator dialog box, you’ll find it more
convenient to click the Edit Queries button (on
the Home tab of the ribbon, in the External Data
group) to open Query Editor and then change
the names of the tables, removing the ContosoBi
prefix. In fact, as you might remember, Query
Editor names them ContosoBi.Sales instead of
the more readable Sales.
After you close Query Editor, Power BI Desktop
loads the table in the model and automatically
creates some relationships among them. The
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Power BI Desktop algorithm that detects
relationships is not perfect, and, in fact, it did not
detect all of the relationships between the tables.
To follow along and catch up to this point, open
Power BI Desktop and then open the companion
content file for Chapter 6: Budget – Start.pbix.
On the navigation bar, David clicks the
Relationship View icon. He then sees the model
illustrated in Figure 6-2 and notices that a
relationship wasn’t created between the Date
and Sales tables.
Figure 6-2: The Power BI Desktop relationship
detector did not find the relationship between Sales
and Date.
This is not an issue; you can easily create the
relationship between Date and Sales by dragging
DateKey from the Sales table to DateKey in the
Date table to link the two tables with the correct
relationship. The final data model is presented in
Figure 6-3. (Note that your view of the data
models might be different; for example, the Date
table might be to the left of Sales, not below it.)
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Figure 6-3: The data model structure is a very simple
schema, with Sales in the middle and the other tables
around it.
Implementing measures
The model, as it is, still requires some
adjustments. First, David hides all of the columns
that should not be visible when creating reports.
He does this by going to Report View, selecting
the table columns, right-clicking one, and then
clicking Hide. David hides all of the keys and the
columns that would be misleading if they were
summed straight.
For example, the Sales table contains Quantity
and Net Price. By default, Power BI offers to
summarize Net Price by summing the values. In
reality, this would be wrong because summing
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the price would not take into consideration the
quantity sold.
Note The Sales table in the Budget – Start.pbix
file is hidden by default because all of its
columns are hidden. To make it visible, in Data
View, right-click the table, and then, on the
shortcut menu that opens, click Unhide All.
The default summarization used by Power BI
works perfectly well when you have a simple
data model. But, as soon as you begin loading
data from relational databases for which
numbers are not stored in such a way as to be
used in Excel workbooks, you need to stop using
default summarization and begin writing DAX
measures, instead. Measures, in DAX parlance,
are scripts that you write using DAX-specific
syntax. By using measures, you can author your
own code and produce much more powerful
data models.
David creates a simple measure to compute the
Sales Amount. In Report View, in the Fields pane,
David right-clicks the Sales table and then clicks
New Measure. In the formula bar above the
canvas in the middle pane, he replaces “Measure
=” with the following code:
Sales Amount = SUMX ( Sales, Sales[Quantity] *
Sales[Unit Price] )
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This measure alone is already extremely
powerful. In fact, because David is now loading
data directly from the data warehouse, he can
slice sales by using any of the available columns,
not just those that were present in the single
view he was using before. For example, now the
Product table contains Category and
Subcategory, which are useful for performing an
analysis of sales in different countries/regions,
with a report such as the one depicted in
Figure 6-4.
Figure 6-4: Using new columns available in the data
model, the reports become more powerful.
Analysis of sales in previous years becomes more
interesting as soon as you have more columns
available. The report shows the relative
contribution of different brands to the
Computers category (notice how the Computers
category is selected in the lower-left bar chart in
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Figure 6-4), whereas the line chart shows the
behavior of sales over time. Different years are
highlighted by different line colors. Using this
tool, David can find an answer to different
questions, like what is the reason for the peak in
September 2013.
Creating calculated
columns
Having more power typically raises the
requirements of the data model. As an example,
consider the line chart: having the sales of the
three years with different lines might be useful
for a comparison of different years; however, if
you want to analyze the behavior of sales over
the three years, it would be much better to show
a single line that spans all of the years.
The problem is that the Date table contains the
month name, and you can easily use it as we did
in Figure 6-4, but if you remove the year from
the legend, you get sales divided by month, not
by month and year, as shown in Figure 6-5.
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Figure 6-5: Slicing sales by month shows the total
sold over all years for each month.
David needs a column containing both year and
month at the same time. Such a column is not
available in the original database. Fortunately, he
has two different options to create this column:
he can use Query Editor to add the column to
the Date query, or he can create a calculated
column.
In Chapter 4, you learned how to use Query
Editor to generate new columns; let’s use this as
an opportunity to learn how to use calculated
columns. To create a new column in a table, on
the Power BI Desktop ribbon, on the Modeling
tab, click New Column, as indicated in Figure 6-6.
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Figure 6-6: You can add new columns to a table by
using the New Column button.
You can add these two columns to the Date
table by typing the following measures in the
formula bar above the table:
Month Year = FORMAT ( 'Date'[Date], "mmm YY" )
Month Year Number = 'Date'[Year] * 100 + MONTH (
'Date'[Date] )
The first column contains a shortened version of
month and year (we keep it short, to make it
suitable for the line chart), whereas the second
column is used to sort the first one, using the
Sort By Column feature we already discussed.
If you now replace the Month Name with the
Month Year as the axis of the line chart, the
visualization is exactly what you want, showing
the behavior of sales over three years, as you can
see in Figure 6-7.
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Figure 6-7: Using a calculated column for the axis of
the line chart leads to the desired visualization.
When building reports, you will typically need a
calculated column to make the visualization look
perfect. Sometimes the descriptions are too
large. In other cases, such as this one, you need a
column representing a specific behavior. Power
BI is an environment in which you model the
data while having the visualization in mind as the
final goal.
Improving the report by
using measures
When you use calculated columns and measures
to perform analyses, you’re limited only by your
imagination. For example, with a few calculations
you can easily build a report like the one shown
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in Figure 6-8, which shows a bubble chart with
the number of products versus the margin
divided by category, where the size of each
bubble is the amount sold.
Figure 6-8: A bubble chart shows a large amount of
information in a single chart, and they are gorgeous
when used with visual interaction and automatic
filtering.
The measures needed to build the report are
very simple:
NumOfProducts = COUNTROWS ( 'Product' )
Gross Margin = SUMX ( Sales, Sales[Quantity] * (
Sales[Unit Price] - Sales[Unit Cost] ) )
NumOfProducts simply counts the number of
products and gives an idea of how many articles
are in the portfolio, whereas Gross Margin
computes the gross margin of sales by
subtracting the cost from the unit price before
multiplying that value by the quantity.
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Note As you have seen so far, we are not
explaining how to write the DAX code. The goal
of this chapter is not to teach DAX; a short
chapter in a short book would not properly
address the complexity of the language. Our
goal here is to show you what you can do as
soon as you begin learning the basics of data
modeling and DAX coding. Incredible analytical
power awaits you when you complete your
journey learning DAX, so hurry and start
learning it today. In the meantime, we will
move on with more complex DAX code and
present some more scenarios that you can
solve by writing simple formulas.
Integrating budget
information
So far, David is excited about the power of his
analytical tool; so much so, in fact, that he’s
forgotten that the task is about budgeting, not
sales analysis. This is one of the major drawbacks
of using Power BI: it is so much fun to dive into
data and analyze it that you might become lost
in evocative reports. Now it’s time to get back to
business and integrate the budget information.
Loading the budget information from Excel is
straightforward, and David has already used the
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technique. But a problem arises as soon as he
looks at the data model. The new table
containing the budget does not have any
relationship with the other tables, as you can see
in Figure 6-9.
Figure 6-9: The Budget table does not have any
relationships with the other tables in the model.
This time, it’s not an issue of Power BI failing to
detect the relationship, which earlier David was
able to correct by using a simple drag-and-drop
technique to establish a relationship. In this
instance, the relationship cannot be created
this way. In fact, when he tries to drag
CountryRegion from the Budget table to
the Store table, he sees the error shown in
Figure 6-10.
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Figure 6-10: Trying to create a relationship between
the Budget and Store tables leads to this error.
The error message suggests that an intermediate
table might help solve the problem. But, before
solving the issue, it’s worth taking a few
moments to understand it better.
You can create a relationship between two tables
if the column you use to create the relationship
is a key in the destination table. You can create a
relationship between the Sales and Date tables
based on the DateKey column because DateKey
has a different value for each row in Date.
Having a different value for each row is the
requisite for a column to be a key. In fact, when
you have a given date, you can uniquely identify
the entire row in Date. In the model with Budget,
CountryRegion is neither a key in the Budget
table, nor in Store. Thus, you cannot create such
a relationship.
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There are multiple ways to solve this problem,
based on the model or on an advanced usage of
the DAX language. The solution based on the
model is somewhat easier to learn and, by the
way, it is the way suggested by the error
message.
If you create a table containing all the possible
values of CountryRegion, CountryRegion
becomes a key for that table. At that point, you
are able to create the relationships between
Budget and Store.
You can build a table containing the possible
values of CountryRegion by using Query Editor,
as you did in Chapter 4, or by adopting a new
technique: a calculated table. Calculated tables
are tables computed using the DAX language
that you can store in the model and use as any
other table. To create a calculated table, on the
Power BI ribbon, on the Modeling tab, click New
Table, and then, in the formula bar, type the
following DAX expression:
CountryRegions =
SUMMARIZE (
UNION (
DISTINCT ( Budget[CountryRegion] ),
DISTINCT ( Store[CountryRegion] )
),
[CountryRegion]
)
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Figure 6-11 shows the resulting table.
Figure 6-11: You can populate a calculated table with
the distinct values of CountryRegion.
Let’s return to see how David is faring. To create
the table, he takes the distinct values of both the
Budget[CountryRegion] and
Store[CountryRegion] columns, forms a union
with the partial results, and finally the Summarize
function returns a summary table of the
CountryRegion column. In this way, all the
possible values will be represented in the
resulting table, either referenced by the Budget
or Store tables.
Now, David has the intermediate table that the
error message in Figure 6-10 suggested to him.
He can create one relationship between Store
and CountryRegions, and another one between
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Budget and CountryRegions, completing the
model. The table is a technical table, which is
useful only to propagate the filter from Store to
the Budget. Figure 6-12 presents the data model
with the new table in place.
Figure 6-12: The CountryRegions table acts as an
intermediate between Store and Budget.
From a technical point of view, David created a
many-to-many relationship between the Budget
and Store tables, using CountryRegions as the
bridge table between them. To test that the
model works well, he creates the following
simple measure that returns the sum of the
budget:
Budget Amount = SUM ( Budget[Budget 2016] )
You can project this measure on a simple report
containing CountryRegion, the value of budget,
and the value of sales. Now, CountryRegion
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correctly slices both Sales Amount and Budget
Amount, as illustrated in Figure 6-13.
Figure 6-13: With the correct data model, now the
numbers are sliced correctly.
The first problem, here, is that the report is not
showing meaningful numbers. In fact, because
there is no filter on the year, it is accumulating
sales over all available years and comparing
them with the budget, which contains forecasts
for 2016 only. You can easily solve the issue by
creating the following measure that computes
the sales amount for only 2015:
Sales 2015 = CALCULATE ( [Sales Amount],
'Date'[Year] = 2015 )
By replacing Sales Amount with Sales 2015 in the
report, the numbers can be compared, as
demonstrated in Figure 6-14.
Figure 6-14: Using Sales 2015 in the report makes
the numbers comparable.
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You also need to apply the same technique to
the other column available in budget, which is
Brand. The DAX code is very similar to the
previous example; you have only to change the
column names to obtain the list of all the
possible brands:
Brands =
SUMMARIZE (
UNION (
DISTINCT ( Budget[Brand] ),
DISTINCT ( Product[Brand] )
),
[Brand]
)
So far, so good. The next step hides a problem
that—again—requires a bit of theory to be
explained.
When you create the relationships between
Product, Budget, and Brands, you end up with a
model like the one depicted in Figure 6-15,
wherein the relationship between Budget and
Brands has been created but remained inactive
(this is because it was the last one we created—
by following a different order in the creation of
relationships, you might obtain the relationship
between Product and Brands as an inactive one).
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Figure 6-15: Among the many relationships, the one
between Brands and Budget is inactive, signified by
the dashed connector line between them.
What is an inactive relationship? It is a
relationship that is present in the model but not
used in the automatic filtering of values. Why did
Power BI Desktop deactivate the last relationship
we created? Because if it did not deactivate it, we
would end up with an ambiguous model.
With the inactive relationship, filtering does not
happen in the correct way. In fact, if you build a
report using countries/regions and brands, the
result is wrong, as demonstrated in Figure 6-16.
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Figure 6-16: Slicing by Brand does not produce a
meaningful result (all of the values are repeated)
because the underlying relationship is inactive.
An ambiguous model is a data model within
which there are multiple paths linking two tables
because all of the relationships are set as
bidirectional (that is, the filter applies in both
directions). (Note that you can tell when a
relationship is bidirectional because there are
two small arrows on the connector line, facing
both directions.) So, where is the ambiguity?
There are many here. For example, if you start
from Product (see Figure 6-15), you can reach
Budget following the bottom chain
Product/Brands/Budget or the upper chain
Product/Sales/Store/CountryRegion/Budget. If
all of the relationships remained active, both
would be legal paths, and you would end up
with ambiguity.
You can solve ambiguity in most cases by just
preventing the path from being traversed, but
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still maintaining the model features. For
example, in the model examined, there is no
need to make Sales filter Store and Sales filter
Product. It is enough that the opposite direction
works in both cases; that is, you can have both
the Store and Product tables filter Sales. To
perform this, you can double-click a relationship
(the connector line between Sales and Store, for
example), which opens the Edit Relationship
dialog box, as shown in Figure 6-17.
Figure 6-17: In the Edit Relationship dialog box, you
can configure many properties of a relationship.
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To disable bidirectional filtering, you must set
the Cross Filter Direction to Single. You should
do this to several relationships: the one between
Sales and Store, the one between Sales and
Product, and to the two linking Budget with
CountryRegions and Brands. The final model is
represented in Figure 6-18.
Figure 6-18: Disabling bidirectional filtering on most
relationships removes the ambiguity.
After you remove ambiguity from the model and
activate the correct set of relationships, the
model works fine. You can test it by building a
simple matrix that shows both Brand and
CountryRegion now filtering the budget
correctly, as illustrated in Figure 6-19.
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Figure 6-19: With the correct model, slicing is
performed the right way.
Reallocating the budget
Budget numbers are correct as long as you slice
by brand or by country/region, which is the
granularity at which the budget is defined.
However, if you add a column that is not a part
of the Budget table (for example, you can slice
by Country/Region and Color), the result will be
wrong. Figure 6-20 shows that values for Black
and Silver colors in United States have the same
value as the grand total of all the colors in
United States.
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Figure 6-20: Slicing at an excessive granularity leads
to incorrect numbers.
In reality, it is not that the number is wrong; it’s
just that it is very difficult to understand what it
is computing. For example, the value in the cell
at the intersection of United States and Black
shows the sum of the budget in the United
States for all the brands that have at least one
product of the color black. Because some colors,
like Black and Silver, are present for every brand,
these rows show the same value of the grand
total.
The number shown is clearly not what you would
like to see. Intuitively, you would like to see the
budget related to only Black products in the
United States. However, with the data model we
have so far, this is not what you obtain.
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The problem is that the budget for black
products (or any other color, for instance) is not
available in the source workbook. There, you
only have the budget for all the products of the
same brand. Nevertheless, even if the number is
not there, you can compute it by using a
technique that is similar to the easy one David
used at the beginning of this book (you might
remember that David sliced the budget by
month and then simply divided that value by 12).
To better understand the technique, let’s begin
with the report presented in Figure 6-21, which
shows budget and sales in a matrix, and a chart
used to filter and show only Contoso’s data.
Figure 6-21: The report shows Budget and Sales
2015 sliced by color for the individual brand, Contoso.
What is the budget value for Black? You can take
the grand total (which is 239,500.00) and
multiply it by an allocation factor computed by
dividing sales of Black in 2015 (49,592.00) by the
grand total of sales (228,978.00). Thus, the
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correction factor is 0.2165, and the value to
display is 51,781.
Using this technique, you allocate the budget
based on sales in the previous year. This time
you take into account the correct seasonality and
any other factors that made higher or lower sales
for a specific color, category, or subcategory.
The budget has a granularity of brand and
country/region. If you focus only on the Brand at
the moment, you can compute the allocation
factor by using the following measure:
AllocationFactor =
DIVIDE (
[Sales 2015],
CALCULATE ( [Sales 2015], ALLEXCEPT ( 'Product',
'Product'[Brand] ) )
)
Figure 6-22 shows the value of such a measure
formatted as a percentage.
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Figure 6-22: The allocation factor is the percentage to
compute Budget Amount for when you analyze the
budget at a lower granularity.
At this point, you can modify the code of Budget
Amount, taking into account the allocation
factor.
Budget Amount = SUM ( Budget[Budget 2016] ) *
[AllocationFactor]
The result, which is shown in Figure 6-23, shows
that now the budget is correctly sliced by Color,
even if the original budget was not.
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Figure 6-23: AllocationFactor is now included in
the formula of Budget Amount.
So far, we have focused only on the Brand, which
is an attribute of the Product table. Alas, the
budget also is defined at the CountryRegion
level, and we need to take this into account. You
need to consider the CountryRegion when
computing the allocation factor, similarly to what
you did for the brand before. These are the final
versions of the formulas to use:
AllocationFactor =
DIVIDE (
[Sales 2015],
CALCULATE (
[Sales 2015],
ALLEXCEPT ( 'Product', 'Product'[Brand] ),
ALLEXCEPT( Store, Store[CountryRegion] ),
ALL ( Date )
)
)
Budget Amount = SUM ( Budget[Budget 2016] ) *
[AllocationFactor]
Figure 6-24 demonstrates that the allocation is
performed against 2015. Notice in the same
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chart on the right, the measures Budget Amount,
Sales 2015, and Sales 2014. Budget follows the
same distribution of Sales 2015 and ignores
Sales 2014, which has a different distribution of
numbers.
Figure 6-24: Distribution of Budget Amount is
identical to Sales 2015 and different from Sales
2014.
Conclusions
As we said in the introduction, in this chapter we
cheated a bit. We did not want to show you
another step-by-step guide to implement
another dashboard. Instead, we wanted to give
you a sneak preview of the capabilities of Power
BI Desktop when you uncover the most
advanced tools, namely:

262
Building a model When you begin
loading the raw tables from the SQL Server
database, instead of predefined queries with
aggregated values, you can perform much
more powerful analyses. At the same time,
you are responsible for handling the data
C H A P T E R 6 | Building a data model
model by yourself. Power BI Desktop offers
you all the tools required to build a complex
data model.


The DAX language DAX is your best friend
in the process of analyzing data. In this
chapter, we used it to create calculated
columns, measures, and calculated tables.
This book is not the proper venue for
explaining how DAX works; that would fill an
entire book by itself. If you are interested in
learning more about DAX, check out our
book The Definitive Guide to DAX (Microsoft
Press, 2015).
Building columns for specific charts
Sometimes, you need a column for an
individual chart. There is nothing wrong with
doing this; you can just build it.
By using some basic skills, you can take Power BI
from a simple reporting tool to what it really is:
an extremely powerful modeling tool with which
you can build gorgeous analyses on top of your
data.
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CHAPTER
7
Improving Power BI
reports
The previous chapters introduce
many features of Power BI that are
related to data modeling,
publishing, and sharing. The focus
in those chapters is on the data
and the numbers, not on the
presentation. Our friend David
took advantage of these features
to create a solution that uses
Power BI to support a
collaborative effort in the yearly
budgeting process for his
company, Contoso. Those
chapters intentionally do not focus
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on the presentation layer or the
visual options of Power BI, but
now it is time to improve the
reports and dashboards in your
models.
This chapter is dedicated to the
visual presentation capabilities of
Power BI. We will use different
dashboards with different datasets
and requirements to show you the
different scenarios and tools
available. For this reason, we’re
going to look beyond David’s
requirements for his solution—we
need a broader scope now. So,
you will learn how to choose
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between the built-in visualizations
available in Power BI, how to use
custom visuals, how to use DAX to
solve common reporting issues,
and, finally, what the correct
approach is for designing highdensity reports.
This chapter is neither a step-by-step tutorial for
using the visual editor in Power BI, nor a
complete visual design patterns guide covering
all the possible types of standard and custom
visualizations. Its goal is to show you the
available options in Power BI and act as an initial
guide to choosing visualization solutions,
depending on your requirements. We will do
that by providing different practical examples
that are available in the companion content, so
that you can open the files and analyze the data
in more detail, reviewing all the properties we
used. In these pages, we provide commentary,
explaining the reason for certain design choices;
you should be able to apply the same principles
when designing your own reports.
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Choosing the right
visualizations
You have several standard visualizations
available in Power BI. In addition, you will see
that you can extend this set by using custom
visualizations. But, before doing that, you need
to know what you can and cannot do using the
standard components, which you can see in
Figure 7-1.
Figure 7-1: The standard visualizations available in
Power BI.
The list of standard visuals includes 27
components in Power BI that are available as of
this writing, but this might expand in future
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versions. Here are descriptions of the standard
components:
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Stacked bar chart Use this when you want
to compare different values of the same
measure, side by side, or when you need to
display different measures that are a part of
the same whole. The bars are horizontally
oriented rows.
Stacked column chart The same as a
stacked bar chart, but vertically oriented.
Clustered bar chart Similar to the stacked
bar chart, but instead of comparing different
measures within the same bar, with a
clustered bar chart you can compare
different measures side by side.
Clustered column chart The same as a
clustered bar chart, but vertically oriented.
100% Stacked bar chart Similar to the
stacked bar chart, but with each measure
using a slice of each bar, which always
corresponds to the entire width available
(100%).
100% Stacked column chart The same as
the 100% stacked column chart, but
vertically oriented.
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Line chart Use this to display the trend of
some measures over time. Usually the y-axis
has a range that does not include zero.
Area chart Similar to the line chart, use
this when you want to display cumulative
data rather than sequences of points.
Usually, the y-axis range begins at zero, and
there is only one measure. This looks like a
line chart wherein the areas are filled with
layers of colors.
Stacked area chart Similar to the area
chart, but with each measure cumulated to
the others.
Line and stacked column chart Use this
when you need to display measures with
different scales, such as currency and
percentage, or different value ranges.
Line and clustered column chart The
same as the line and stacked column chart,
but using clustered columns instead of
stacked columns.
Waterfall chart Use this to display
cumulative data, highlighting for each value
its positive or negative value. The initial and
final value columns usually start on the
horizontal access, with color-coded floating
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columns between them, making it look like a
waterfall or bridge.
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270
Scatter chart Use this when you want to
show possible correlations between two
measures.
Pie chart Use this to display the
distribution of values of one or more
measures. The values appear as pieces of the
pie, with the larger values taking up larger
slices. However, using pie charts is not a best
practice.
Treemap Similar to a pie chart, but using a
rather different graphical representation,
wherein the values are represented by
colored rectangles on a page. It could be an
alternative to a pie chart, but it is equally
unreadable when you have many elements
in it.
Map Use this to display geographical data
with variable-sized circular shapes on Bing
maps.
Table Use this to display data in a textual
form as a simple table, where every attribute
and every measure is a single column in the
result.
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Matrix This extends the table, making it
possible to group the measures by rows and
columns.
Filled map Similar to the map, but the
data is represented by colored overlay areas.
Funnel Similar to the stacked bar chart, but
with a single measure and a different
graphical representation, wherein the rows
are stacked in order, which makes the chart
look like a funnel.
Gauge Use this to display a single measure
against a goal. This chart resembles a gauge
style that is common in cars.
Multi-row card Use this to display
different measures and attributes for each
instance of an entity, each placed on a
different colored and graphical card.
Card Use this to display a single numerical
value of a measure textually, placed on a
colored and graphical card.
KPI Use this to display a single value with a
trend line chart in the background,
highlighting its performance with colors.
Slicer Use this to filter one or more charts
by selecting values of an attribute.
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Donut chart Similar to the pie chart, but
with a donut or tire-like graphical
representation. However, using donut charts
is not a best practice.
R script visual Use this to display charts
generated by R-language code.
The first design principle is simple: just because
you have many components, there is no reason
for you to use all of them. In a single report, the
presence of many different types of components
can be confusing. Thus, do not put too many
different visualizations in the same report
without a good reason. Moreover, the default
properties of a component are not necessarily
the right choice for your report, so you should
consider modifying their value to better display
your data. You will see several examples of these
principles applied to our first report example.
For instance, consider a dashboard displaying
Sales for Contoso. By choosing the right
visualization and setting the right colors, you can
obtain a good presentation of your data,
focusing your viewer’s attention on the data
instead of the visualization itself. You can see a
first version of this dashboard in Figure 7-2, in
which sales amount, margin, and target values
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are sliced by date, brand, subcategory, and class.
This dashboard is available on the page “Sales
2015” of the Sample-Sales.pbix file included in
the companion content.
Figure 7-2: First version of a report displaying
Contoso’s sales in 2015 using standard visualizations.
The first thing to notice in Figure 7-2 is that
there are only two colors used in the report:
black and yellow. Items in black identify the sales
amount measure, whereas items in yellow are for
other comparison measures (target, sales cost,
and margin percent, depending on the
visualization).
The four charts in the report use a limited
number of common visualizations: line chart, bar
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chart, and columns chart. The rationale behind
each visualization choice is described in the next
section, but in general it is better to use simple,
well-known visualizations when that is sufficient
to produce the display of useful and
understandable information.
Choosing between standard
visuals
The first chart in Figure 7-2 compares Sales
Amount and the Target values using a simple
line chart. Figure 7-3 shows this in more detail.
Figure 7-3: A line chart of sales amount by date.
The line chart is the primary choice when you
display a measure over a date range or time,
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using the x-axis for the temporal dimension. You
can select the colors of the different measures by
using the Data Colors properties, as
demonstrated in Figure 7-4. Here you can select
the color of each measure included in the line
chart.
Figure 7-4: The Data Colors properties for a line chart
visualization.
You can use a slight variation of the line chart
when a measure that you want to display is part
of another measure. For example, consider the
Sales Amount and Sales Cost measures.
Hopefully, Sales Cost is always lower than Sales
Amount, and the graphical distance between
them represents this margin. With the line chart,
the gap between the two measures might not be
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clear, so you might want to “paint” the area
below the line by using the values of the
measures along time. The area chart does exactly
this, as illustrated in Figure 7-5.
Figure 7-5: An area chart of sales amount versus
sales cost by date.
The y-axis must begin at zero; otherwise, the
area would not be fully representative of the two
values. The visible gray area expresses the delta
between the two measures in a graphical way
and corresponds to the margin. You should not
use an area chart when you have several
intersections between different lines. You should
consider it only when measures do not intersect
often. The example in Figure 7-5 demonstrates
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one of the few cases for which you can consider
using it.
Note For the sample reports in this chapter, for
the most part we do not use pie charts and
donut charts. This is because they are not
considered a best practice, with an exception
that you will see in the last section. The human
brain can more easily make comparisons
between lengths (as in a bar chart) than
between angles (as in pie and donut charts).
Comparing measures with different scales
requires particular visualizations. You need to
display two y-axes, and you need a way to easily
associate the axis corresponding to each
measure. For example, Figure 7-6 shows a line
and stacked column chart that yields more
details by displaying the sales amount measure
divided by category and class, compared with
the margin percent divided by category.
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Figure 7-6: A line and stacked column chart of sales
amount and margin percent by category and class.
The scale of the main measure (sales amount) is
represented on the left y-axis, and the other
measure (margin percent) is on the right y-axis.
The x-axis shows the name of the category
corresponding to each column, which is also
divided by class using different shades of gray.
Figure 7-7 shows the properties of this
component used to bind data. The x-axis is
called the shared axis, and it can include more
than one attribute. In this case, we used two
product attributes: Category and Subcategory.
This makes it possible to perform an interactive
drill-down of data in Power BI.
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Figure 7-7: Data binding properties for a line and
stacked column chart.
You can activate the drill-down feature for the
selected column chart by clicking the drill down
button (the down-arrow icon) located in the
upper-right corner of the visualization. When
drill-down mode is turned on, the drill-down
button changes to display a black background,
as depicted in Figure 7-8.
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Figure 7-8: The drill-down button in a visualization.
The black background signifies that drill-down mode is
turned on.
With drill-down mode activated, when you click
a column within the chart, you can drill down to
that column’s respective subcategories. Figure 79 shows the resulting chart when you click the
Computers column in Figure 7-6. Note all of the
subcategories that are related to the Computers
category.
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Figure 7-9: A line and stacked column chart of sales
amount and margin percent by subcategory and class
for the Computers category.
To drill up, in the upper-left corner of the
visualization, click the drill-up button (the uparrow icon), as highlighted in Figure 7-10.
Figure 7-10: The drill-up button in a visualization.
After you move up to the product category
again, you can drill down to all of the
subcategories for every category by selecting the
double-arrow button in the upper-left corner of
the visualization, as illustrated in Figure 7-11.
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Figure 7-11: The drill-down button in a visualization.
When you drill down to the subcategory level for
all the categories, you obtain a chart similar to
that shown in Figure 7-12. Both the Sales
Amount and Margin percentage measures have
the subcategories granularity in the chart, so you
can still compare them.
Figure 7-12: A line and stacked column chart of sales
amount and margin percent by subcategory and class,
for all the categories.
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More info You can find an animated guide on
how to use the drill-down feature in Power BI at
https://powerbi.microsoft.com/documentation/
powerbi-service-drill-down-in-a-visualization/.
Using custom
visualizations
Power BI provides a number of embedded
visualizations that are ready to use. By changing
properties of the existing components, you can
create solid presentations of your data. However,
sometimes you might want to present data in a
way that is not possible using the standard
components. Power BI provides you with a
gallery of custom visualizations that have been
created by members of the Power BI community.
You can download and install one or more of
these custom visualizations in your report. To
view a gallery of custom visualizations, go to
https://app.powerbi.com/visuals/.
In this section, we will improve Power BI reports
using features available in selected custom
visualizations. The goal is to show you how to
include custom visualizations in your report and
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what improvement you can achieve by using
them. We suggest that you visit the gallery of
available components because we cannot cover
all the custom visualizations that are available,
and new custom visualizations are published
weekly.
First steps with custom
visualizations
The first improvement we want to apply to the
dashboard in the Sample-Sales.pbix file is
coloring Sales Amount and Margin % according
to their value. Using the standard card
visualization, these values have a fixed color, as
depicted in Figure 7-13.
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Figure 7-13: Sales Amount and Margin % displayed
in a standard card visualization.
You can change the color in the Data Label
properties, but the choice would be static. We
would like to dynamically change the color so
that the Sales Amount is green when it increases
more than 20 percent compared to the value of
the previous year, and the margin percentage is
green when it is higher than 130 percent; yellow
when it is between 100 percent and 130 percent;
and red when it is lower than 100 percent (you
might want to use a red color only for negative
margins in other scenarios; we use ranges that
adapt to this specific example).
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To achieve this goal, you need a custom
visualization that dynamically changes the color
of the displayed value, based on a particular
state. In the custom visual gallery, you can
choose the Card With States By SQLBI
visualization, as illustrated in Figure 7-14.
Figure 7-14: A description of the custom visualization
Card With States By SQLBI.
After you download the visualization, you save
the file using the .pbiviz extension (Power BI
Visual). Then, in the Visualizations pane, you can
import that visualization in your report by
clicking the Import From File button (the ellipsis),
which is highlighted in Figure 7-15.
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More info You can find a more detailed guide
describing how to import a custom
visualization at
https://powerbi.microsoft.com/documentation/
powerbi-custom-visuals-use/.
Figure 7-15: The Import From File button (ellipsis) in
the Visualizations pane.
Note The file Sample-Sales.pbix already
includes the custom visualizations used in this
example. You should already see the
corresponding icons without having to install
them.
If you select the card visualization displaying
Sales Amount, you can change it to Card With
States By SQLBI by clicking its button in the
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Visualizations pane, which became available after
you imported the custom visual. Figure 7-16
shows the new button.
Figure 7-16: The button for the imported Card With
States By SQLBI visualization.
In the Fields pane of the component (left side,
Figure 7-17), you set State Value to YOY %,
which is a measure defined in the data model
that provides the year-over-year growth as a
percentage. We want a red color if the growth is
negative, yellow if the growth is positive and less
than or equal to 20 percent, and green if the
growth is greater than 20 percent. Set the To
Value property (shown in the right pane in
Figure 7-17) of the State 2 section to 0.2 (which
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corresponds to a 20 percent increase), and the
From Value property of the State 3 section to
0.2, as well.
Figure 7-17: The Fields and Format panes for the
Card With States By SQLBI visualization.
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You repeat the same operation for the card
visualization displaying Margin %: changing it to
Card With States By SQLBI, setting the State
Value field to the Margin % measure, and setting
To Value in the State 2 properties to 1.3, and
also changing From Value in State 3 to 1.3. In
this way, you will display the Margin % using the
same value displayed, whereas the Sales Amount
will be colored, based on a year-over-year
growth percentage. Figure 7-18 shows the final
result. By changing the selection of months,
categories, and brands, you might see different
colors.
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Figure 7-18: The Sales Amount and Margin
percentage using Card With States By SQLBI.
We used this simple example to explain the
process of importing and using custom
visualizations in your report. In the following
sections, we will focus more on considerations
about when a custom visualization is useful or
even required.
Improving reports by using
custom visualizations
One of the charts available in the initial
dashboard (the clustered bar chart in the lowerright corner of Figure 7-2) shows the sales
amount divided by brand. This chart, as with all
of the others in the dashboard, is dynamically
updated whenever you select a different month
or an element in other charts in the same
dashboard. For this reason, the values displayed
correspond to the sales filtered by the current
selections in the charts and slicers in the
dashboard. However, you might want to
compare the sales amount of each brand with
the corresponding sales made one year before
as well as with the goal defined for the same
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selection. This initial chart displays only the sales
amount, as shown in Figure 7-19.
Figure 7-19: A clustered bar chart of sales amount by
brand.
If you want to display other measures in the
same chart, you need to add them. Each
measure will have a different bar, and you should
set different colors to recognize each measure.
For example, Figure 7-20 shows the goal
measure and the sales amount measures for the
years 2014 and 2015 (previously, we were
displaying the sales amount only for the year
2015).
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Figure 7-20: A clustered bar chart of sales amount
and target by brand.
Note You might wonder how we displayed two
values of the same measure (sales amount) in
the same chart with two different names. In
Power BI Desktop, you can create new
measures in the data model, so you can simply
assign an existing measure to a new measure
with a different name and then assign the new
measure to the visualization. You will find other
considerations about using DAX to improve
reports in the section “Using DAX in data
models,” later in this chapter.
You might consider using a different
visualization to improve the readability of this
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chart. For example, by using the custom Bullet
Chart By SQLBI, you can obtain the results shown
in Figure 7-21. The value of the sales amount for
2015 is a bar in the middle, surrounded by a
shorter bar (overlapping on top of it) that has
the sales amount for 2014, along with a short,
black vertical line that acts as a marker for the
goal value. The different graphics simplify the
way the reader recognizes the more important
value and the terms of comparison.
Figure 7-21: The Bullet Chart By SQLBI visualization,
which displays actual sales amounts and goals by
brand.
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The only issue in this visualization is that we
used black for the Target marker and yellow for
the 2015 sales amount, which inverts the choice
made in other charts of the same report. The
reason for this is if we applied this color choice
to other charts, it would have produced a barely
readable marker for the goal value. In this case,
the custom visualization can slightly improve the
visualization, but it is not strictly necessary.
Figure 7-22 demonstrates the final result of the
Sample-Sales.pbix report, using the two custom
visualizations that we’ve used thus far.
Figure 7-22: The final version of the report, displaying
Contoso’s sales in May, 2015. This version uses
standard and custom visualizations.
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Let’s look at another example to see how using a
custom visualization can improve a report.
Figure 7-23 shows a report that represents the
density of the population in each state within the
United States, using the standard filled-map
visualization. Darker shades correspond to
higher-density values; lighter shades correspond
to lower-density values.
Figure 7-23: A report displaying population density
using a filled-map visualization.
This particular visualization (on the right of
Figure 7-23) does not include the name of each
state, because more than 95 percent of the
states are condensed in less than 20 percent of
the available real estate in the chart, so there
simply is not enough room.
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You can represent the same information by
using a custom map and moving Alaska and
Hawaii in a different position, changing also their
size and making the map more readable. You
can do this by using the Synoptic Panel By SQLBI
visualization component that you can find in the
visualization gallery. With this component, you
can draw custom areas over any map image.
(There is a gallery of maps ready to use at
http://synoptic.design.) Figure 7-24 shows where
you can find and download a map of the United
States that includes the state names.
Figure 7-24: The gallery of country/region maps that
are available on the Synoptic Designer website.
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By using the Synoptic Design panel with the
customized map of the United States, you can
create the map shown in Figure 7-25, in which
each state displays its name as well as its
respective shade of gray. Moreover, the Synoptic
Design panel also can work offline, whereas
displaying the standard map component
requires an active Internet connection to query
the Bing service.
Figure 7-25: The same report displaying population
density, but this time using a panel from Synoptic
Design with a custom map of the United States.
Here again, you have seen how to improve a
report by using a custom visualization, but until
now we never had the need for a custom
visualization to show the desired data. The
standard components always offered an
alternative way for you to display the same data
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that, even if not ideal, you could use if custom
visualizations are not available. In the next
section, you will see some examples for which a
custom visualization is required to achieve a
minimum goal.
Identifying conditions when
custom visualizations are required
In this section, we use a different report to show
conditions for which different visualization
choices can produce more- or less-meaningful
results. The report we want to consider shows
the status of a stocks portfolio, using historical
prices of stocks to display charts describing the
behaviors of different shares and of the entire
portfolio. Figure 7-26 depicts the initial version
of this report.
Note You can find this report on the page
Stocks in the Sample-Stocks.pbix file.
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Figure 7-26: A report displaying stock portfolio
performance over time using standard visualizations.
The report includes a line chart for each share,
representing the closing price of the
corresponding ticker. The details of the portfolio
are included in a simple table in the upper-left
corner, which displays for each share the
quantity owned in the portfolio and its
corresponding value at current prices. There is
also a line chart for the entire portfolio, in which
each share is represented by a single line of a
different color. The line represents the total value
of such a share in the portfolio over time. This
chart can be useful to identify which stock has
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the largest value in the portfolio over time, but it
does not provide in any way an indication of the
total value of the portfolio over time. However,
you can change this visualization to the stacked
area chart depicted in Figure 7-27 to obtain a
better representation of the portfolio’s value.
Figure 7-27: The same portfolio value shown in
Figure 7-26, but now shown over time, divided by
share name.
Every color represents a different share, so you
also have a rough estimation of the weight of
each stock in the portfolio. The main difference
between a stacked area chart and a line chart is
that the former cumulates the value of each
share name, whereas the latter displays each
share name individually. In this case, an accurate
choice of the visualization between the existing
ones can satisfy the presentation requirements.
However, in the other charts, a standard Power
BI visualization is not very useful.
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The four charts displaying the stock price for
each day do not provide complete information.
The data model actually contains different price
values for each day: open price, minimum price,
maximum price, and close price. It is very
common to display these four values for each
period considered (a day, in this example) by
using a candlestick chart. This chart type is not
available in the standard Power BI visualizations,
so you need to install a custom visualization for
that. For example, the Candlestick by SQLBI
visualization provides a basic visualization that
includes four measures for each period: Open,
Close, High, and Low. Figure 7-28 presents the
final result of the report after applying the two
changes described in this section.
Figure 7-28: The same stock portfolio report
displaying performance over time using custom
visualizations.
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You have seen how custom visualizations can
improve the report, and sometimes they are
necessary to achieve the desired graphical result.
From time to time, though, you might still need
to massage the data model to present measures
and attributes to visualizations in the expected
way, using the right granularity, and the
expected formatting. The DAX language is your
best friend here, as you will see in the next
section.
Using DAX in data
models
So far in this chapter, we have presented several
examples of visualizations. We’ve demonstrated
how you can improve your reports by choosing
the right visualization, setting the properties in
an appropriate way, and even installing custom
visualizations when required. However, there are
a number of improvements that you can achieve
that do not require a direct action on the
visualizations. You can instead create measures
or calculated columns in DAX. Usually, you use
DAX to obtain a certain numeric result, but
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sometimes you can take advantage of a DAX
expression to control the report layout.
Our first example concerns the measures used in
the candlestick charts of the report shown in
Figure 7-28. Every time period displayed involves
four measures: Open, Close, High, and Low. The
data recorded in the data model has four
corresponding columns for each day and stock.
However, you might use the candlestick chart to
display data by week or by month instead of by
day. The aggregation required for each measure
depends on the measure itself. The Open
measure’s price must be the DayOpen value of
the first day in the period, the Close price must
be the DayClose value of the last day in the
period, the High price must be the maximum
value of DayHigh in the period, and the Low
price must be the minimum value of DayLow in
the period. You can write these four measures by
using the following DAX expressions:
Open =
IF (
HASONEVALUE( StocksPrices[Date] ),
VALUES ( StocksPrices[DayOpen] ),
CALCULATE ( VALUES ( StocksPrices[DayOpen] ),
FIRSTDATE ( StocksPrices[Date] ) )
)
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Close =
IF (
HASONEVALUE( StocksPrices[Date] ),
VALUES ( StocksPrices[DayClose] ),
CALCULATE ( VALUES ( StocksPrices[DayClose] ),
LASTDATE ( StocksPrices[Date] ) )
)
High = MAX ( StocksPrices[DayHigh] )
Low = MIN ( StocksPrices[DayLow] )
As mentioned earlier in this chapter, another
useful tip is to create a measure just to display a
measure with a different name. The reason is
that many visualization components use the
measure name in a legend or other description,
and you do not have a way to rename that by
using visualization properties. For instance, if you
have two measures, Current and Previous, but
you want to display the exact year in a particular
visualization, you might create two measures
with the exact year that you want to display in
the legend, as in the following example:
[2014] = [Previous]
[2015] = [Current]
In a report that you will see in the next section,
we will use data extracted from Google Analytics.
The Website table contains the columns Users
and Country ISO Code. In the dashboard, it will
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be interesting to show on a map the ratio
between the number of users visiting a website
and the population of the country/region where
users come from, but such information is not
available directly from Google Analytics. You can
import the information about countries’/regions’
populations in a separate Countries/Regions
table and link it to the Website table using the
Country ISO Code column, as illustrated in
Figure 7-29.
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Figure 7-29: A schema of tables and relationships of
a data model that extends Google Analytics website
data.
Because the ratio would be a decimal number,
you can create a metric called Users Per Million
by using the following measure definition in
DAX:
Users per Million =
DIVIDE (
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SUM ( 'Website'[Users] ),
SUM ( 'Countries'[Population] )
) * 1000000
You should not expect that a visualization
component would directly do a calculation, such
as a ratio or a difference. It is always better to
define the corresponding calculation in a DAX
measure, and then you bind that measure to the
visualization.
Finally, you should consider using a calculated
column when you need a classification that
groups existing data with a high granularity to a
lower number of unique values that are easier to
display in a chart. For example, Figure 7-30
shows the existing values in the Browser Size
column that is provided by Google Analytics.
There are more than 5,000 unique combinations
of width and height, and this fragmentation of
values makes the analysis harder. Moreover,
each resolution is a single string, and the reports
should group different resolutions by width,
ignoring the height.
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Figure 7-30: A partial list of values included in the
Browser Size column.
You can split the problem into two steps. First,
you extract the width size from the string,
converting the digits before the “x” character in
an integer number. Then, you compare this
number with a list of predefined values
representing the interesting range of resolutions
you want to analyze, such as 1024, 1280, 1440,
1920, and 2560. You can see the two calculations
implemented in the following two measures,
Width Size and Width Category, respectively:
Width Size =
VAR xPos = FIND ( "x", Website[Browser Size], , 0 )
VAR widthString = IF ( xPos > 1, LEFT (
Website[Browser Size] , xPos - 1 ), "" )
RETURN IF ( widthString <> "", INT ( widthString ) )
Width Category =
SWITCH (
TRUE(),
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Website[Width Size] <= 1024, 1024,
Website[Width Size] <= 1280, 1280,
Website[Width Size] <= 1440, 1440,
Website[Width Size] <= 1920, 1920,
Website[Width Size] <= 2560, 2560,
CALCULATE ( MAX ( Website[Width Size] ), ALL (
Website ) )
)
Figure 7-31 presents the results of the two
calculated columns. We will use the Width
Category in one of the visualizations described in
the next section.
Figure 7-31: A partial list of values included in the
Browser Size, Width Size, and Width Category
columns.
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Creating high-density
reports
In the last section of this chapter, we want to
discuss the challenges that you face when
creating high-density reports. When you include
many visualizations in a single report, you need
to carefully balance the amount of information
provided in each of those visualizations,
removing all the unnecessary elements that
would reduce the attention of the user. You want
your user to focus only on the data. As you will
see, having an idea of the overall structure and
tuning properties of each visualization is much
more important than using particular custom
visualizations, which are usually the icing on
the cake.
Figure 7-32 depicts a first version of a report
showing website data from Google Analytics for
the DAX Formatter website, which is available in
the companion content, in the file SampleDAXFormatter-Analytics.pbix file. The report
contains 28 visualizations with data, plus one
slicer and eight components without data that
are there only for aesthetic reasons (title, logo,
pictures, and separators). The 28 visualizations
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only use 7 different visualization types, and some
of them are simple variations of the same
concept, such as stacked/clustered bar/column
charts. You can obtain a complete and complex
report using only a few component types.
Figure 7-32: A high-density report based on Google
Analytics data.
The entire report is organized in three zones: left,
center, and right. The left zone contains metrics
regarding the number of users, the center zone
shows data about the sessions, and the right
zone includes technical information about
average page load time, device type, operating
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system, browser, and resolution used by website
visitors.
If you were to try to create a similar report
starting from scratch, you would need to apply
the following guidelines:




313
Reduce text Include only the minimum
necessary of textual elements, avoiding
repetitive or verbose descriptions.
Remove legends Whenever possible,
avoid including a legend, especially
whenever there is only one measure
displayed in the chart.
Remove axes In a compact visualization
for which you already included the data
labels (setting the Data Labels property to
On), you can remove corresponding axes. All
the clustered bar charts in the report are
formatted in this way.
Use images to explain concepts Use an
icon or a meaningful image related to the
data you show. Remember, a picture is worth
a thousand words. In the example in Figure
7-32, at the top of the report is one different
image for each of the three zones (Users,
Sessions, and Average Page Load Time).
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In the report, we used a donut chart. Previously
in this chapter, we mentioned that using a pie
chart or a donut chart is not a good idea, so you
should avoid doing that. The exception we
wanted to include in this report is when you
compare only two values. In this example, we
used a donut chart for the search engines
distribution, showing the percentage of sessions
coming from Bing searches versus Google
searches. It is clear that Google has a clear
leadership for directing visitors to this website,
with Bing producing only a marginal
contribution. For this difference, looking at the
exact number or percentage is not relevant.
There are three visualizations that we wanted to
improve, starting from this initial example. The
sparklines used at the top of the report are
simple line charts without any axes, legend, data
labels, or border. Figure 7-33 illustrates that we
set to Off most of the format properties of those
line charts.
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Figure 7-33: The Properties pane of a line chart used
as a sparkline.
However, you cannot change the line width of
the line chart. When you use the line chart in a
small area, this produces a result that is difficult
to read. You can replace the line chart with the
Sparkline custom visualization that you can
download from the Power BI visualization
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gallery. Figure 7-34 shows a side-by-side
comparison of the same chart displayed by using
a line chart (on the left) and a sparkline custom
visualization (on the right). The custom
visualization draws a line with a smaller width,
generating a final result that is more readable.
Figure 7-34: A couple of examples of the same chart,
using a standard line chart and a sparkline custom
visualization.
Another visualization to improve is the
countries/regions penetration displayed in the
lower-left corner of the report. Instead of using a
standard map, which shows a pie for each
country/region with a size depending on the
population density, you can use the Synoptic
Designs panel, loading the world map from the
gallery shown back in Figure 7-24. The result of
displaying the measure Users Per Million in a
Synoptic Designs pane is visible in Figure 7-35.
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Figure 7-35: Synoptic Designs panel with a world
map that displays the ratio between users and
population.
The last visualization to improve is the one in the
lower-right corner, showing the number of
sessions by browser resolution. In the original
data, we have a very fragmented number of
different resolutions, so the bar chart only
displays the first values, but the number of
sessions of the most common resolution is just
five percent of the total number of sessions, so
there are a wide number of different resolutions
considered, most of them not visible in the
report. We solved this problem in two steps.
First, we created a column containing the width
category, which classifies the width extracted
from the resolution string, as you have seen in
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the previous section about DAX. Then, we
changed the clustered bar chart to a waterfall
chart, using the Sessions % measure instead of
the Sessions measure, as shown in Figure 7-36.
Figure 7-36: A waterfall chart with the distribution of
sessions by browser resolution.
We do not use any decreasing step in the
waterfall chart, but the final result clearly shows
the distribution of the resolution in a meaningful
way. The Sessions % measure is a DAX
expression created just for this report, using the
following definition:
Sessions % =
DIVIDE (
SUM ( Website[Sessions] ),
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CALCULATE (
SUM ( Website[Sessions] ),
ALL ( Website[Width Category] )
)
)
Figure 7-37 presents the final result, after these
improvements have been incorporated. As you
can see, the overall difference is an incremental
improvement and not a substantial change from
the result we got initially by using standard
visualization components.
Figure 7-37: A high-density report based on Google
Analytics data.
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Especially in a high-density report, you should
focus on the overall quality and readability,
reducing the number of distractions for the
reader. There is already an overwhelming
amount of information, so the focus of the
reader should be entirely on data, not
decorations or visualizations that are too
complex and do not provide any added value.
Conclusions
In this chapter, you have seen a number of
techniques to improve Power BI reports by
choosing the best built-in visualizations and
adding custom visualizations. Here are the main
steps in this process:


320
Choose the right visualization type You
can choose between many visualization
types, but usually you do not need too many
of them in the same report. Do not be afraid
of using the same visualization type many
times if it is the one that best displays your
data.
Customize visualization properties You
can customize every visualization by using
format properties. Using a consistent color
scheme is one of the most important aspects
of a good report.
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


321
Consider custom visualizations when
necessary The Power BI custom
visualizations gallery provides you with many
visualizations that extend the set of standard
ones available in Power BI. You should
consider using them when there is a
concrete advantage over the standard
visualizations.
Use DAX to create measures and
calculated columns You also can use DAX
expressions to achieve the desired
visualization. Even a simple transformation,
such as renaming a measure in a legend, is
not always possible within the properties of
a visualization, but you can overcome this by
creating new measures and calculated
columns.
Remove unnecessary elements in high
density reports In a high-density report,
you need to remove any graphical element
that is not necessary to communicate
information to the users; you do not want to
distract them by including details that do not
provide any useful information.
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These guidelines are just a starting point in your
journey to create clear and useful reports. Your
experience, the feedback from users of your
reports, and the analysis of reports created by
other people are the other important steps along
this road.
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CHAPTER
8
Using Microsoft
Power BI in your
company
With Power BI, you can create a
dashboard using some data in a
Microsoft Excel workbook, or you
can connect to existing structured
databases in your company. The
previous chapters in this book
demonstrate how you can create
data models, reports, and
dashboards, gathering information
from different data sources. They
also show you how you can
consume these results on different
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company
devices. This chapter shows you
techniques to obtain a deeper
integration with existing systems
and applications.
You can get data from many existing data
sources in your company. You can embed a
dashboard visualization in Microsoft Office with
minimal effort. If you have developer skills, you
also can take advantage of the REST API to
automate operations in Power BI by
implementing real-time updates of dashboards,
with no more latency than just a few seconds.
Power BI is a platform that exposes several
services through a REST API that is easy to use
by any application, including standard webbased applications and those running on mobile
devices. In this chapter, we will look at some
example results that will help you to understand
why the presence of this extensibility option is so
important. The goal is not to describe in detail
how to use these APIs from a developer’s point
of view, but to make you aware of what you can
obtain by using them. If you are a developer, you
will find some links and references to additional
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company
material to understand how to use the existing
API. If you are a business user or a BI architect,
you will gain a fuller understanding and know
what you can and cannot ask a developer.
However, keep in mind that the API for Power BI
is undergoing constant evolution. If something is
not possible as of this writing, it might become
possible in a future release. When in doubt,
check what new features are available in the API.
Getting data from
existing systems
Any company has a number of existing data
sources that you can use in Power BI. You have
seen that you can create a data model in Power
BI by copying the content of tables that exist in
other databases or files. You also have the
option of refreshing this content dynamically, or
you can directly query the data source whenever
you access a report. By querying directly, you
avoid the need to create a copy of the data that
you must then synchronize periodically. In this
section, you will see the available options with
which you can connect Power BI to either your
on-premises database or a database in the
cloud.
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company
Before looking at the details, here are a few
terms with which you should be familiar:




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On-premises If you get data from a
database that is physically stored in a server
managed by your company, we say that the
database is on-premises (often shortened to
on-prem).
Cloud If you get data from a Microsoft
Azure service, you are using data in the
cloud. Cloud computing accesses and uses
shared compute and storage resources on
the Internet.
Relational database This is a database
that stores data using tables that have
relationships with one another. Typically,
you query this by using the SQL language.
Examples of on-premises relational
databases that Power BI supports are
Microsoft SQL Server, Microsoft Access,
Oracle, IBM DB2, MySQL, PostgreSQL,
Sybase, and Teradata. Cloud-based relational
databases that Power BI supports include
Azure SQL Database and Azure SQL Data
Warehouse.
Rich semantic model This is a database
that stores both data and metadata,
simplifying navigation by using tools such as
C H A P T E R 8 | Using Microsoft Power BI in your
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Excel PivotTables and Power BI reports. A
typical example is Microsoft SQL Server
Analysis Services. Other supported providers
are SAP HANA and SAP Business Warehouse.


Power BI Personal Gateway This is a
component installed on the user’s computer
that makes it possible to perform data
refreshes on models published using the
Power BI service. (Chapter 3 explains how to
install this.) A Personal Gateway serves only
one user, and only when the user’s computer
is turned on.
Power BI Enterprise Gateway This is a
component similar to the Personal Gateway
that a system administrator installs on a
server in your company. A single Enterprise
Gateway can serve all the users of a
company, and it is also available as soon as
the server is turned on (servers are usually
active 24/7). You can find more technical
details about how to install it at
https://powerbi.microsoft.com/documentati
on/powerbi-gateway-enterprise/.
There is also another concept to clarify before
moving forward, which is the difference between
a model requiring data refresh and a live
connection.
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Understanding differences
between data refresh and live
connections
When you navigate in data via Power BI, you can
read a copy of the data stored in Power BI (either
the Power BI service or the Power BI Desktop
application), or you can have a live connection
that sends a real-time query to the data source
without creating a copy of the data.
Chapter 4 shows that when you connect Power
BI to a SQL Server database, you have two
connection settings from which to choose:
Import and DirectQuery. The DirectQuery option
does not create a copy of the data in Power BI,
and it translates any user action made navigating
on a report into one or more queries to SQL
Server. In a more general classification, we can
say that you can either import data in Power BI
or connect to the data source via a “live
connection.” With relational databases,
DirectQuery is the tool used to obtain a live
connection to the data source. As you will see
later in this chapter, in Power BI you have similar
options when you connect to a SQL Server
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Analysis Services data source: Connect Live and
Import Data.
Regardless of the underlying database, when you
create a model in Power BI by importing data,
you have full access to the features of Power BI.
However, you need to run or schedule a data
refresh to keep data updated on Power BI.
On the other hand, when you use a live
connection, your Power BI model can have only
one data source, so a single Power BI report
cannot mix visualizations connected to data
coming from different data sources. To do that,
you must import data into Power BI. In a
dashboard, however, you can always include
visualizations from different reports; thus, you
can combine visualizations from different live
connections in a dashboard only.
In the following sections, you will see in more
detail how to use live and imported data sources
using existing databases and models in your
company.
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Using relational databases
on-premises
When you create a data model in Power BI
Desktop, you often get data from an onpremises relational database. For example,
Chapter 4 demonstrates how to create a new
Power BI Desktop data model that gets data
from a Microsoft SQL Server database. In this
case, you use a database on-premises, and you
can choose between the Import and DirectQuery
connection types. The former creates a copy of
the data in the Power BI Desktop model, which
requires a refresh in order to synchronize the
content of the Power BI data model with the
source database. The latter does not create a
copy of the data; instead, Power BI generates
queries to SQL Server every time you navigate in
a report.
In both cases, after you publish the Power BI
Desktop file to the Power BI service, the refresh
operation requires either a Personal Gateway or
an Enterprise Gateway. If you use the Personal
Gateway, you can only refresh datasets created
by importing data, but you cannot use the
DirectQuery option. To publish a Power BI
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Desktop data model created by using
DirectQuery, you need the Power BI Enterprise
Gateway.
Figure 8-1 illustrates what happens when you
publish a Power BI data model connected to onpremises data sources via the Import connection
type. A copy of the data and the description of
the data model are stored in the Microsoft cloud.
The data is always available to queries sent by
any client, which sees data updated on the last
data refresh. You need a Power BI gateway
installed on-premises to complete the data
refresh: either a Personal Gateway or an
Enterprise Gateway, in this scenario.
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Figure 8-1: A Power BI gateway is required to refresh
a Power BI model, getting data from on-premises
databases.
Figure 8-2 shows what happens when you
publish a Power BI model created by using the
DirectQuery option. The Power BI service does
not store a copy of data in the Microsoft cloud; it
has only a semantic description of the data
model, with the information required to retrieve
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data from the original source database. Every
time the Power BI service receives a query, it
generates one or more queries in SQL language
and sends these requests to the relational data
source through the Power BI Enterprise Gateway.
More info The Microsoft cloud service does
not preserve any data received from the
relational databases on-premises; it might only
keep a transient data cache on a volatile device
in order to improve the performance of other
queries sent by the same user. You can find
more information in the Power BI Security
whitepaper published by Microsoft at
http://download.microsoft.com/download/4/8/
C/48CFCF8A-2025-4B97-B249-7B505E26E7ED/
Power%20BI%20Security%20Whitepaper.docx.
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Figure 8-2: For on-premises data sources, you must
have the Power BI Enterprise Gateway for a model
using the DirectQuery connection setting.
As of April 2016, you can take advantage of
DirectQuery on SQL Server, Oracle, or Teradata
relational databases, which are all supported in
the Power BI Enterprise Gateway.
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Using relational databases in the
cloud
If you create a Power BI model that uses a
relational database stored in the cloud, you
might not need the Power BI Gateway to refresh
data. Power BI supports direct connection to
Azure SQL Database and Azure SQL Data
Warehouse data sources, so you can schedule a
data refresh or you can use DirectQuery without
the need to install and configure any gateway.
You will still have a different architecture,
depending on which connection setting you use,
Import or DirectQuery. Figure 8-3 illustrates that
by using the Import setting you still have a copy
of data owned by the Power BI service, but you
can refresh that copy without any gateway if the
data source is Azure SQL Database or Azure SQL
Data Warehouse.
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Figure 8-3: Power BI can connect directly to Azure
SQL Database and Azure SQL Data Warehouse to
refresh a Power BI model.
Figure 8-4 illustrates the behavior of Power BI
using DirectQuery connected to Azure SQL
Database or Azure SQL Data Warehouse. As with
any DirectQuery connection, the Power BI service
has only a semantic description of the data
model, along with the information required to
retrieve data from the original source database.
It does not store a copy of data in the Microsoft
cloud. Every time the Power BI service receives a
query, Power BI generates one or more SQL
queries and sends these requests to the
relational data source, with no gateway required.
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Figure 8-4: In DirectQuery mode, Power BI connects
directly to Azure SQL Database and Azure SQL Data
Warehouse.
If you have a cloud-based relational database,
other than Azure SQL Database and Azure SQL
Data Warehouse, you must use the architecture
for on-premises data, and you need to install a
gateway to complete the refresh operation or to
implement DirectQuery.
Note Multiple requests on different servers in
different locations might increase the latency of
requests, so you might want to consider
installing the Enterprise Gateway on a server
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hosted in Azure Virtual Machines to improve
the performance.
Using live connections to Analysis
Services
When you create a live connection to Analysis
Services in Power BI Desktop, you do not create
a data model, and you do not have a copy of the
data in the PBIX file. Thus, when you publish the
model on the Power BI service, the PBIX file
contains only the definition of the reports, but
the entities are defined in the Analysis Services
file. When you edit a report in Power BI that is
tied to a live connection to Analysis Services, all
of the operations requested by the client are
redirected to Analysis Services through the
Power BI Enterprise Gateway, as shown in
Figure 8-5.
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Figure 8-5: When a model has a live connection,
Power BI redirects a query to Analysis Services onpremises.
The Power BI service contains neither data nor
metadata for the data model. Any change made
to the data model in Analysis Services is
automatically reflected in Power BI, without
requiring a data refresh operation in Power BI.
However, keep in mind that usually Analysis
Services has a copy of the data read from one or
more data sources (unless you create a data
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model in Analysis Services by using DirectQuery),
so you need to refresh the model in Analysis
Services to keep it up to date.
Integrating Power BI
with Office
Showing dashboards and reports created in
Power BI using one of the options available (web
browser, dedicated apps on mobile devices, and
Power BI Desktop) is very useful. However,
perhaps you want to create a particular report in
Excel or a presentation in Microsoft PowerPoint,
which could benefit from a tighter integration
with Power BI. As you will see in this section,
there are several features in Power BI that can
take advantage of such a service in certain
applications of Office.
Publish Excel data models in
Power BI
When you publish a Power BI Desktop file to
Power BI, you are copying to the cloud a file
containing a data model, a copy of the data, the
query to import and refresh the data, and all the
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reports you created. If you have an Excel file with
a data model, you have a similar file, and you can
publish such a file on Power BI, as well. In fact,
the following correspondence exists between
features in Power BI and Excel:



Power BI data model  Excel Data
Model (also known as Power Pivot data
model)
Power BI Query Editor  Workbook
Queries (formerly known as Power Query
in Excel 2010 and Excel 2013)
Power BI report  Power View
You can load into Power BI an XLSX file
containing a data model instead of a .pbix file. In
doing this, you keep all of the existing features
and reports in your Excel file, but you also can
then use the same data model in Power BI. All
the existing reports in Power View are converted
in equivalent reports in Power BI whenever
possible (certain features of Power View might
not have a corresponding visualization or feature
in Power BI). In this scenario, the PivotTables and
PivotCharts you have in Excel continue to work
with the Power Pivot data model. Figure 8-6
presents the Publish To Power BI feature that is
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available in Excel 2016, which guides you in
publishing a Power Pivot data model to Power BI
without even opening the Power BI website,
similar to how you would publish within Power BI
Desktop.
Figure 8-6: The Publish To Power BI feature in
Excel 2016.
It is important to consider that, if you import the
Power Pivot data model in Power BI Desktop,
you will have a .pbix file instead of an .xlsx file. In
this way, you can work locally with Power BI, but
you will have two copies of the same data model
and the same data. You will use Excel to navigate
data with PivotTables and PivotCharts, and you
will use Power BI to navigate using dashboards
and reports. However, if you publish a .pbix file,
you also can consume your data in Excel using
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the Analyze In Excel feature, which is described
in the next section.
Consume Power BI content from
Excel
In this book, you have seen how to import data
from Excel to Power BI by using different
techniques. However, you might want to move
data in the opposite direction, consuming in
Excel data that is published on Power BI. This is
indeed possible, and you can do it by using the
Analyze In Excel feature that is available in the
Power BI service. Figure 8-7 depicts the Analyze
In Excel action that is available for datasets and
reports.
Figure 8-7: The Analyze In Excel feature in Power BI.
When you click Analyze In Excel, you might be
prompted to install an updated driver for Excel; if
this happens, follow the instructions to install the
suggested driver. In any case, this action
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downloads a small file on your computer (with
an .odc extension) that Excel uses to open a
connection to the corresponding Power BI
model, creating a PivotTable on top of the
model. Figure 8-8 shows the result if you request
Analyze In Excel on the dataset Google Analytics.
Figure 8-8: A PivotTable in Excel that is connected to
the Google Analytics model in Power BI by using
Analyze In Excel.
Note By establishing a connection using
Analyze In Excel, you are consuming your
Power BI model as if it were an external
analytical database. This is the same behavior
you have in Excel when you connect to an
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Analysis Services database, or you connect
Excel to a Power Pivot model published on
SharePoint by using the Excel document URL as
the server name. The Analyze In Excel feature
uses Excel only as a client, without storing the
data model within Excel, as you would do when
you are using Power Pivot for Excel.
After you establish the connection with a
PivotTable, you have all the tables, columns, and
measures of the Power BI data model listed in
the PivotTable fields. You can use only the
measures that have been explicitly defined in the
data model, so you cannot create measures
during the navigation as you can do in Power BI.
For this reason, it is important that you create all
the measures that could be necessary to an Excel
user, without assuming that any numeric column
can be aggregated or that a measure can be
created upon request. For example, the Sales
2015 – Analytics model that David created in
Chapter 5 does not contain any explicit
measures, and this makes it difficult to use in
Excel. Figure 8-9 illustrates how the data of such
a model is presented in the Excel PivotTable
Fields pane.
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Figure 8-9: A PivotTable in Excel that is connected to
the Sales 2015 - Analytics model in Power BI by using
Analyze In Excel.
The model without explicit measures is not very
useful in Excel, because you do not have any
calculations to put in the Values area of the
PivotTable. For example, Figure 8-10 shows the
result of selecting the columns CountryRegion,
Sales2013, and Sales2014 from the PivotTable
Fields pane. These columns are placed in the
Rows area of the PivotTable, and you cannot
move them in the Values area. Thus, the result is
a list of all the unique values of these columns,
grouped by CountryRegion, Sales2013, and
Sales2014.
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Figure 8-10: A PivotTable in Excel that is connected
to the Sales 2015 - Analytics model in Power BI by
using Analyze In Excel.
Excel ignores that the nature of the sales
columns is that of numbers that can be
aggregated. This information must be provided
by using explicit measures, such as those
demonstrated in Chapter 6. For example, Figure
8-11 shows a PivotTable obtained by using
Analyze In Excel on the Budget data model
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created in Chapter 6 and published on Power BI.
In this case, the measure Sales Amount is
aggregated by Brand and Year.
Figure 8-11: A PivotTable in Excel that is connected
to the Budget model in Power BI by using Analyze In
Excel.
One of the reasons to analyze data in Excel is to
take advantage of specific Excel features. For
example, in Figure 8-11 we applied a conditional
formatting rule, so that higher values have a
green background color, and smaller values have
a red background color.
You might have many other reasons to use Excel
to analyze a Power BI model. In general, Excel is
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a flexible application with which you can easily
integrate data coming from different sources
with data and/or calculations you have in the
Excel file. Moreover, as of this writing, many of
the features of a PivotTable in Excel are not yet
available in the Power BI visualizations. Thanks to
Analyze In Excel, you have the maximum
flexibility to combine different clients (Power BI
and Excel, for example) to analyze the data of
the same data model.
Note The authentication used to connect Excel
to the Power BI model requires a new version
of the OLE DB driver that is used to establish a
connection to an external Analysis Services
database (OLE DB for OLAP). For this reason,
you might be prompted to install such a driver
the first time you use Analyze In Excel on a
computer. The connection to Power BI uses
claims-based authentication, which is a
different technology than the Windows
integrated security that you might be using to
connect to Analysis Services. You might be
prompted to provide a user name and
password to connect to Power BI the first time
you open such a connection. If you need to
connect to different Power BI models using
different users, you might need to modify the
connections string manually, even if this
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behavior might change in the future, because
the Analyze In Excel feature is in preview mode
as of this writing. For updated documentation,
go to https://powerbi.microsoft.com/
documentation/powerbi-service-analyze-inexcel/.
Using Power BI Tiles from Office
Store
You can create an Office document in Excel and
PowerPoint in which you embed one or more
Power BI visualizations. You can create a
PowerPoint presentation in which you show live
data from Power BI. In a similar way, you can
create an Excel workbook in which you embed
some visualizations from Power BI on the same
page where you also have other data presented
with standard Excel tools. This is possible thanks
to a free third-party add-in called Power BI Tiles,
which takes advantage of the Power BI APIs
(these APIs will be explained in more detail later
in this chapter). If you are not a developer, you
still might be interested in the technical details;
you just want to use the existing tool.
You can download Power BI Tiles from the Office
Store. It is compatible with Microsoft Office 2013
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Service Pack 1, or any following version,
including Office 2016. If you have a subscription
to Office 365 that includes the licensing of
desktop applications, you should already have a
compatible version of Office installed on your
computer.
Note Power BI Tiles is a free add-in created by
DevScope; it is not a Microsoft product, but as
of this writing, there are no corresponding
solutions produced by Microsoft. You do not
need administrative rights to install Power BI
Tiles.
Let’s visit with David once again, and consider
how he can create a PowerPoint presentation
that embeds some of the visualizations he
created while working on the budget. In
PowerPoint, on the ribbon, on the Insert tab,
David clicks the Store button, as shown in
Figure 8-12.
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Figure 8-12: The Store button on the PowerPoint
ribbon.
In the Office Add-Ins dialog box, in the search
box, David types “power bi tiles,” which presents
him with the result depicted in Figure 8-13.
Figure 8-13: A list of available Office add-ins, filtered
by the string “power bi tiles” in the search box.
Note If you do not have PowerPoint installed
locally and you want to use Office online, you
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can still use the add-ins available in the Office
Store by using the online version of the Office
application.
After David installs the add-in, Power BI Tiles
becomes available on the PowerPoint ribbon, on
the Insert tab, in the My Add-Ins list, as
demonstrated in Figure 8-14.
Figure 8-14: The Power BI Tiles add-in on the
PowerPoint ribbon in the My Add-Ins list.
When David clicks Power BI Tiles in the list of My
Add-Ins, PowerPoint inserts a new rectangular
object in the current slide that will display the
content of a report or a dashboard. Within this
area, you are prompted to choose between
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connecting to your Power BI account or
displaying a public report (Chapter 2 explains
how to publish a report to a webpage for public
access), as shown in Figure 8-15.
Figure 8-15: You can choose between connecting to
a Power BI account or public reports for Power BI
Tiles.
The first time David connects to Power BI, he is
asked to grant authorization for Power BI Tiles to
access certain Power BI features, as shown in
Figure 8-16.
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Figure 8-16: Power BI authorization request for
Power BI Tiles access.
David clicks Accept, which authorizes Power BI to
accept requests coming from Power BI Tiles. He
now can use dashboards and reports available in
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any Power BI workspaces to which he has access.
David can select either a dashboard or a report,
and he needs to consider that there are a few
differences in the visualizations and user
interactions between the two. For example,
Figure 8-17 depicts the list of reports available to
David in the Power BI Tiles visualization in a
PowerPoint slide. In this example, he chose the
Budget 2016 group workspace, using the middle
button of the three in the upper-right corner of
the area used by the Power BI add-in. The list of
reports displays the only report published in the
workspace: Budget Totals. If you want to see the
list of available dashboards in the same
workspace, click the Dashboards button, located
directly to the left of Reports, above the report
list.
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Figure 8-17: A Power BI Tiles object inserted in a
PowerPoint slide lists the available reports to display.
After David selects a report, it is rendered within
the workspace of the PowerPoint slide. If the
report size is larger than the available space,
scrollbars will appear, as shown in Figure 8-18.
The report embedded by Power BI Tiles is fully
functional and interactive, so David can use
filters and zoom single visualizations of the
report, exactly as he can do on the Power BI
website. He can even utilize this interaction in
Slide Show mode.
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Figure 8-18: A slide in PowerPoint that embeds the
content of the Budget Totals report.
You can change the objects displayed in the
Power BI Tiles add-in by going back to the list of
dashboards and reports (refer back to Figure 817). To do that, below the report, click the Back
button (the left arrow), which is the one farthest
left.
If you select a dashboard, you obtain a slightly
different behavior: the Power BI Tiles add-in
displays only one visualization from a dashboard
at a time. If the dashboard contains two or more
visualizations, arrows will appear on the left and
right side of each one, which you can click to
scroll through the visualizations, as illustrated in
Figure 8-19. In this example, only one of the two
available visualizations in the Budget Totals
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dashboard (which, by the way, are the same
visualizations used in the report) is visible on the
slide. Each visualization takes up the entire
amount of space available, which makes them
easier to view, but you do not have any
interaction with the charts.
Figure 8-19: A slide in PowerPoint with embedded
visualizations of the Budget Totals dashboard.
Whether you choose to embed a dashboard or a
report depends on the type of presentation you
are creating. If you want to interact with the
data, you can modify either choice during the
presentation, navigating in the list of dashboards
and reports available. However, you should
choose the visualization that is more readable
and effective for your presentation.
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If you want to refresh the data during the
presentation, click the Refresh button, which is
the second one from the right, below the Power
BI Tiles add-in (see Figure 8-19).
You also can use the Power BI Tiles add-in in
Excel, creating a worksheet that displays a
visualization from Power BI next to data
rendered in Excel; for example, using a
PivotTable. The features of this add-in work well
in Office online, too.
Managing security to
access data
The previous chapters of this book
demonstrated several ways to share data with
other users, within and outside of your
organization. In this section, you will review the
features available in Power BI to share data with
other users and to control access to data in a
more granular way, up to the row level in each
table.
The following list explains the visibility options
available to a Power BI user (other options
through APIs are described later in this chapter):
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


361
My Workspace All of the datasets, reports,
and dashboards you save in My Workspace
are visible only to you, unless you explicitly
share them by using one of the sharing
features that follow.
Group Workspace All of the datasets,
reports, and dashboards saved in a group
workspace are visible to all the members of
the group. The group in Power BI
corresponds to a group in Office 365, so you
can administer the group from both
administrative user interfaces. A new
member in the group automatically has
access to the data available to the group.
Share dashboard When you share a
dashboard, you send a personal invitation to
a single individual identified by an email
address. This email must correspond to a
Power BI account. If the invited user
connects to Power BI for the first time, he
can create a Power BI sign-in to access data.
You can control who has access to a
dashboard, adding and removing users from
the allowed list at any time. Optionally, you
can choose to delegate another user, who
can then share the dashboard to other users.
When you share a dashboard with a user,
you also provide him read-only access to all
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the underlying reports (from pinned tiles)
and datasets used in the dashboard. In fact,
users can freely navigate in data sources by
starting with a request in the Q&A question
box and then customizing filters and visuals,
selecting different slices of data and
changing the measures displayed and
attributes analyzed.


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Create content pack You can publish an
organizational content pack, sharing
datasets, reports, and dashboards, at the
granularity that you prefer. When you
include a report in a content pack, you also
automatically share the underlying datasets.
When you include a dashboard in a content
pack, you also share the underlying reports
and datasets. Those who use an
organizational content pack have access to
all of the data included in the content pack
and can freely navigate to them, creating
new reports and dashboards based on that
data.
Publish to the web You can publish a
report to the web, embedding it within a
custom webpage on a website you can edit,
or by simply providing a URL containing just
the published report. Any user can access
that report through this URL, and she can
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interact with the report using only the
visualizations displayed in the report itself.
The user cannot gain access to the
underlying data source, and cannot modify
measures, slicers, filters, and visualizations
used in the report. You cannot control who
accesses the report, because the URL can be
freely shared with anyone; in fact, Microsoft
can publish the report in a public gallery.
You should not use this feature to distribute
reports that contain sensitive data.

Row-level security You can restrict data
access for specific users by defining filters at
the row level in one or more tables of a
dataset. This is an additional security level
applied to users who already have access to
the data because you shared a dashboard
with them. For example, you might share the
same dashboard with five different
managers, one for each country/region,
letting them see only the data of the
country/region that they manage.
When you choose the method by which you
want to share data with other users, you need to
evaluate the visibility you want to provide to
reports and datasets, and the type of restrictions
you want to apply. In the previous chapters, we
describe each of the aforementioned features
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except the last one, which is the topic of the next
section.
Using row-level security
When you want to restrict the rows visible to a
single user, you must apply a security rule to the
dataset, so that regardless of the report
displayed or edited, the user cannot access data
that he is not allowed to see. For example, the
managers of China or Europe should see only
data relevant to their area, even if all of them use
the same report. This type of security is known as
row-level security.
If you are using a live connection to Analysis
Services or you created a model by using
DirectQuery, you must implement row-level
security on the source database, and you cannot
modify its behavior in Power BI.
If you have a model that imported data in the
Power BI service, you can apply row-level
security to the dataset. You can manage rowlevel security by selecting the Security action
available for datasets, as you can see in
Figure 8-20.
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Figure 8-20: Selecting Security to activate row-level
security on a dataset.
In the row-level security configuration, you
create one or more security roles, which define
the rows a user can see in each table. You can
find a step-by-step guide to configuring rowlevel security at
https://powerbi.microsoft.com/documentation/p
owerbi-admin-rls/.
Note As of this writing, this feature is in
preview, and details might change very quickly.
You might want to refer to the online
documentation to see if there have been any
updates to the user interface.
Figure 8-21 shows the final result of a security
role (named China) providing access to only
those rows corresponding to sales made in
China.
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Figure 8-21: The configuration for row-level security
for limiting access to sales made only in China.
Each role contains one or more members; these
are the users who can access the model through
the role. The rules defined for the role are in the
form of a DAX expression for each table. A row
in a table is visible if the condition, for that row,
is true. If a user belongs to more than one role,
he will have access to all the rows that are visible
in at least one of his roles. However, if a user
does not belong to any security role and the
dataset has row-level security active, he will not
see any data for that dataset, regardless of
whether he can access the dashboard containing
data from that dataset because it has been
shared by another user.
When you restrict access to a table that has a
one-to-many relationship with other tables, you
restrict access to the related tables, too. Consider
a model with two tables: Customers and Sales.
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Applying a security rule to Customers also
restricts Sales, showing only the sales related to
a visible customer.
Let’s take a look at what happens when David
creates the row-level security rule shown in
Figure 8-21, assigning Wendy Kahn as a member
of the role, and then he shares with her the
dashboard depicted in Figure 8-22.
Figure 8-22: A dashboard shared by David, showing
China, Germany, and the United States.
When Wendy opens the same dashboard shared
by David, she sees only China; Germany and the
United States are not visible, as illustrated in
Figure 8-23.
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Figure 8-23: A dashboard displayed to Wendy,
showing only China.
The security restrictions applied to the
dashboard Wendy received from David are also
applied to any other visualization shown to
Wendy and are based on the same dataset. By
applying security at the row level, you can easily
customize the aggregations visible to each user.
However, keep in mind that you cannot prevent
a user from viewing a particular table, column, or
measure. The row-level security filters rows, not
columns or other entities of a certain dataset.
When you share a dashboard and its reports, in
theory the user consuming the data cannot gain
access to other entities (measures, columns) that
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were not published in the report, but this is not
guaranteed by the row-level security filter and
cannot be enforced at the dataset level. For this
reason, if you need to ensure that certain
measures are not visible to a group of users, you
should consider creating a separate model for
that, including only columns and measures that
can be made visible to all of the users who can
access a certain dataset.
Note As of this writing, the row-level security
feature is in preview. It has a number of
restrictions that might be lifted or removed in
subsequent releases. As of now, you can apply
row-level security only in datasets included in
My Workspace, but not in group workspaces
and not in datasets published in content packs.
You can add only single users as role members,
not user groups or distribution lists. You can
apply it only to datasets created by using
Power BI Desktop, not to datasets created with
Power Pivot for Excel (but you can import such
a model in Power BI Desktop and then publish
the Power BI Desktop file). However, when you
publish a new version of the Power BI Desktop
file, all the existing security roles are removed
completely. DirectQuery is not supported for
row-level security. Q&A and Cortana are not
supported by row-level security, so Q&A input
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is not visible if role-level security is active for all
the models related to a shared dashboard.
Extending and
customizing Power BI
Power BI is not only a service that you can
activate and use. Likewise, it is not only a
product (Power BI Desktop) that you can
download and install. You can extend and
customize Power BI in many ways, because
Power BI offers a number of extensibility points
to developers who are interested in adding
features, customizing the experience, or
integrating existing applications with Power BI.
You can find a more detailed introduction
oriented to developers at
https://powerbi.microsoft.com/documentation/p
owerbi-developer-overview-of-power-bi-restapi/. The goal of this section is to introduce you
to what is possible and what the current
limitations are. In this way, you will have a better
understanding of the platform before looking for
developers who might help you in this effort, if
you do not have the required skills but are
interested in achieving the results.
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Creating custom visualizations for
Power BI
Chapter 7 describes how to add custom
visualizations in a Power BI report. As a user, you
are likely interested in using existing custom
visualizations, and you can find a public gallery
of them at https://visuals.powerbi.com. If you
need specific visualizations that are not available
in the gallery, you can (or ask a programmer to)
create a new visualization, following the guide
available at
https://powerbi.microsoft.com/documentation/p
owerbi-custom-visuals/. The main skills required
to create a custom visualization are TypeScript (a
typed version of JavaScript) and CSS. Thus, if you
have programming skills in JavaScript and CSS,
you will have a short learning curve to become
proficient in writing code for a custom
visualization. To become inspired to create new
visualizations in Power BI, take a look at the
examples of custom visualizations in Chapter 7.
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Introducing the Power BI REST
API
Previously in this chapter, we showed you how to
use the Power BI Tiles add-in. Recall that this
component interacts with the data and services
provided by Power BI using a programming
interface called REST API. In this book, we do not
want to go into the details of a REST API or how
to use it, but you can find these details in the
online documentation. Most of the information
there is useful to developers who want to
integrate Power BI services in their applications.
The goal here is to explain the importance of this
API and why it is the foundation of an ecosystem
that makes the integration between Power BI
and other applications and services possible,
going beyond the features currently available
to Power BI end users.
Using the REST API for Power BI, a developer can
create a new application or extend an existing
one so that it can publish or consume data,
reports, and dashboards in Power BI. REST
stands for Representational State Transfer,
which is a protocol that allows any existing
programming language to interact with the API,
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and it is widely adopted in modern
programming platforms. REST facilitates
interoperability across different languages and
operating systems. There are particular
specifications to handle authentication and
authorization, and you can find all of the
details and many examples at
https://msdn.microsoft.com/library/dn877544.as
px.
Besides the REST protocol, the API exposes
several features that can manipulate the
following entities:



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Dataset You can create new datasets and
read existing ones.
Table You can create new tables and
modify the schema of existing ones that you
created before using the same API. You
cannot modify tables that are a part of a
data model that imports data from external
data sources.
Row You can add and delete rows in tables
that you created in a dataset. The delete
operation removes all the rows, and you
cannot specify any filter, whereas the add
operation works incrementally, adding new
rows to the existing ones.
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



Group You can access a particular group
to create a dataset in a group instead of a
personal workspace.
Import You can import a Power BI Desktop
model (.pbix file) or a Power Pivot for Excel
data model (.xlsx file) in Power BI.
Dashboard You can retrieve dashboards
and tiles from dashboards from a particular
workspace to which you have access. The
Power BI Tiles add-in, for example, uses this
API to retrieve the selected visualization
from a dashboard.
Report You can retrieve reports from a
particular workspace to which you have
access.
You can integrate the visualizations and report
objects that you can access through the API in
an existing application (this is called “embedding
an IFrame”), which is actually what the Power BI
add-in does. In the following sections, you will
see two examples of applications that are
possible thanks to the Power BI REST API.
Limitations
It is worth mentioning the current limitations,
considering that the API will evolve and new
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features will be added, hopefully also to cover
some of the scenarios that are not available
today. In general, you cannot manipulate the
content of a single object. For example, you
cannot alter a published dataset, dashboard, or
report. You cannot create a report or a
dashboard programmatically. You can publish a
Power BI Desktop file (.pbix), which can include
particular reports, but you do not have an API to
create a report or a .pbix file programmatically.
This is a current limitation for the Power BI
embedded scenario that is described in the next
section; but keep in mind that it is still in preview
as of this writing, and new API features certainly
will be added in the future. Thus, you’ll probably
want to look at the updated documentation to
check whether new features have been added to
overcome the limitations described here.
By using the Power BI REST API, you can extend
existing applications, integrating features
available in the Power BI service. This API also
opens myriad possibilities to third-party vendors
to create components, applications, and services
that extend the features available in Power BI.
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Pushing real-time data to Power
BI dashboards
One of the features available by using the Power
BI API is the ability to “push” data in a dashboard
in real time. By using this feature, the numbers
and visualizations included in a dashboard are
automatically updated almost every second,
reflecting the changes received in the data.
However, the datasets underlying these
dashboards are designed in a particular way, and
in this section, we want to give you an overall
view of the features and limitations of this
technique. If you’re a developer who
is interested in creating these dashboards, a
complete walkthrough is available at
https://powerbi.microsoft.com/documentation/p
owerbi-developer-walkthrough-push-data/.
To obtain a real-time dashboard, you first need
to programmatically create a dataset. Then, you
need to build reports using this dataset, and
finally you can pin report visualizations and Q&A
visualizations in the dashboard. These tiles will
be automatically updated as soon as the
underlying dataset receives new data.
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The first step also defines the biggest limitation
that exists for real-time dashboards: You must
first create a dataset programmatically, and this
dataset can have tables and columns that will be
filled with data sent by an application. The data
refresh is not possible in these datasets. We call
this a push mode, where an application sends
data to Power BI, instead of having Power BI ask
for data from the data source (which is the
classic pull mode used by data refresh). You
cannot create the data model by using Power BI
Desktop, and you cannot add relationships and
measures to the data model. You can obtain only
standard aggregations for measures, such as
sum, average, count distinct, and other
predefined ones, but you cannot create either
calculated columns or measures using custom
DAX expressions. For this reason, it is difficult to
display percentages and variations obtained by
aggregating existing data. The dataset can be
created in a personal workspace or in a group
workspace.
The second step, which still requires that you or
an application developer write custom code, is to
insert rows in the tables of the dataset using the
Power BI REST API. Every table has a limit of
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5,000,000 rows, or 200,000 rows if you choose a
storage model (also known as FIFO dataset) that
automatically removes the older rows. The
amount of rows written and the frequency of
update depends entirely on the application that
“pushes” data into the dataset in Power BI. A few
limitations exist, based on the Power BI plan
used (10,000 rows per hour for the free service
plan, and 1,000,000 rows per hour for the paid
service plan, Power BI Pro). You can write this
code specifically for a single dataset, or you can
take advantage of the Azure Stream Analytics
service, which simplifies using the same stream
of data in different datasets, as described at
https://azure.microsoft.com/
documentation/articles/stream-analytics-powerbi-dashboard/.
After a dataset is created and populated with
data, you can create a report using Power BI
online. You cannot use Power BI Desktop for
such an operation. You can use all the
visualizations and filters available in a report to
create your visualization. A report is not updated
in real time; you must always manually refresh it
to display the updated data. You can pin every
visualization used in the report to a dashboard
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visualization, and when you establish the
connection between the dashboard and the
dataset (by pinning the first visualization), you
can also begin using Q&A to navigate the data
and to obtain other visuals that you can pin to
the dashboard. All the tiles in the dashboard that
are connected to a dataset that receive data in
push mode, automatically refresh their content
as soon as new data is received. You might
observe a refresh almost every second, and the
latency between updates of data and
visualizations is typically only a few seconds.
Figure 8-24 presents an example of the real-time
dashboard we created to monitor the usage of
the DAX Formatter service, which is available at
www.daxformatter.com. Every time a user
formats a DAX query using this service, the
application updates the dataset on Power BI,
providing the date and time of the request and
a flag that specifies whether the request was
formatted correctly (increasing the Formatted
counter) or contained a syntax error (increasing
the Errors counter).
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Figure 8-24: A dashboard updated in real time with
data from www.daxformatter.com.
It is interesting to note that using Q&A is
important to obtain certain dynamic filters,
which are impossible to build in a report because
you cannot create custom measures in DAX in
this type of dataset. For example, the number of
requests made today can be obtained by asking
the question through Q&A, as illustrated in
Figure 8-25. The word “today” is converted to
the current date and is applied as a filter to the
date column, returning the number of requests
made in the current day. Note also that the
number of requests in Figure 8-25 increased
from Figure 8-24 because data is continuously
updated in the dataset!
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Figure 8-25: Request made through Q&A for the
number of requests made today.
Power BI embedded in
applications
Another extension available through the Power
BI REST API and specific libraries to manage
authentication for custom application is Power BI
Embedded, which is an Azure service with which
you can set up integration between an
application and Power BI services.
For example, consider a service that collects data
about personal bicycle trips. This service has a
web application to manage the configuration
and manual upload of data, even if most of the
information will be sent by specific devices
and/or apps. When it comes time to analyze
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data, it would be nice for this application to use
Power BI services. One way to obtain this
integration is to export data to Power BI and
create custom data models and reports. For the
company that provides this service, it could be a
good idea to create a service content pack to
make it easy for existing Power BI users to
import their data. However, this approach
requires all users to create their own Power BI
account (even if it is a free one), whereas the
application instead should have some embedded
solution to display standard reports containing
personal data. By using Power BI Embedded, the
developers who build the web application can
design these reports and publish them within
their application in a seamless way. In this way,
the web application can show a report
containing data of the user who signed in within
the same webpage, and without requiring
additional authentication to the user itself. The
process is completely transparent to the user.
More info You can find documentation for
Power BI Embedded and its pricing details at
https://azure.microsoft.com/services/power-biembedded/.
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Note It is worth to mention that we showed
you another type of web publishing in Chapter
2, the Publish To Web feature. The main
difference between Power BI Embedded and
Publish To Web is that the former controls the
authentication of the user and can display
customized content within the same report,
whereas the latter only shows the same content
to anonymous users.
Conclusions
In this chapter, you learned how you can use
Power BI in your company, what the architectural
implications of the many options available for
data refresh are, how to manage security, and
what the options are to customize Power BI
and/or integrate it with existing applications.
Here are the most important features you
learned:



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Available options to update data with
scheduled refresh or live connections
Integration of Power BI with Microsoft Office
Control data access for specific users with
row-level security
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
Possible extensibility options using the
Power BI REST API
Power BI is an open ecosystem that is constantly
growing, thanks to the features added by
Microsoft and those additional options provided
by third-party groups, which use the same API
you can use to customize and extend Power BI
according to your specific needs.
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About the authors
Marco Russo and
Alberto Ferrari
are the founders of
sqlbi.com, where
they regularly
publish articles
about Microsoft
Power BI, Power
Pivot, DAX, and
SQL Server
Analysis Services.
They have worked with DAX since the first beta
version of Power Pivot in 2009, and, during these
years, sqlbi.com became one of the major
sources for DAX articles and tutorials.
They both provide consultancy and mentoring
on business intelligence (BI), with a particular
specialization in the Microsoft technologies
related to BI. They have written several books
and papers about Power Pivot, DAX, and Analysis
Services. They also wrote popular white papers
such as “The Many-to-Many Revolution” (about
modeling patterns using many-to-many
relationships) and “Using Tabular Models in a
Large-Scale Commercial Solution” (a case study
of Analysis Services adoption published by
Microsoft). Marco and Alberto are also regular
speakers at major international conferences,
including Microsoft Ignite, PASS Summit, and
SQLBits.
You can contact Marco at
marco.russo@sqlbi.com, and Alberto at
alberto.ferrari@sqlbi.com.
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