Learn power BI a beginner’s guide to developing interactive business intelligence

Power BI Pro Power BI Premium Power BI Embedded Power BI Report Server Power BI Desktop and ServicePower BI Desktop Getting data Creating a data model Analyzing data Creating and publish

Copyright © 2019 Packt Publishing

All rights reserved No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews Every effort has been made in the preparation of this book to ensure the accuracy of the information presented However, the information contained in this book is sold without warranty, either express or implied Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals However, Packt Publishing cannot guarantee the accuracy of this information.

Packt.com

Subscribe to our online digital library for full access to over 7,000 books andvideos, as well as industry leading tools to help you plan your personal

development and advance your career For more information, please visit ourwebsite

Spend less time learning and more time coding with practical eBooks andVideos from over 4,000 industry professionals

At www.packt.com, you can also read a collection of free technical articles, sign upfor a range of free newsletters, and receive exclusive discounts and offers onPackt books and eBooks

Contributors

Greg Deckler is Vice President of Cloud Services at Fusion Alliance and has

been a technology systems consultant for over 25 years Internationally

recognized as an expert in Power BI, Greg Deckler is a Microsoft MVP for DataPlatform and an active member of the Power BI community, with over 100,000messages read, more than 11,000 replies, over 2,200 answers, and more than 75entries in the Quick Measures Gallery Greg founded the Columbus Azure MLand Power BI User Group (CAMLPUG) and presents at numerous conferencesand events, including SQL Saturday, DogFood, and Dynamic Communities' UserGroup/Power Platform Summit

I would like to thank the dynamic and vibrant Power BI community as a whole, and especially Charles Sterling, for their dedication and support Also, shout-outs to the following Power BI Community members:

@ImkeF, @konstantinos, @parry2k, @Seth_C_Bauer, @Phil_Seamark, @GilbertQ, @Vvelarde,

@MattAllington @marcorusso, and @Mike_Carlo.

Peter Ter Braake started working as a developer in 1996 after studying physics

in Utrecht, the Netherlands Databases and business intelligence piqued his

interest the most, leading to him specializing in SQL Server and its businessintelligence components He worked with Power BI from the tool's very

beginnings

Peter started working as an independent contractor in 2008 This enables him todivide his time between teaching data-related classes, consulting with customers,and writing articles and books

Vishwanath Muzumdar has 6 years' experience in information technology

consulting, business analysis, business development, and business process

management in the business intelligence space He is a Microsoft Power BIdeveloper and creates powerful visual reports for his clients while implementingcorporate Power BI solutions and user training He also has expertise in realizingclient requirements across multiple domains, and proficiently planning and

executing strategies for the same at both an individual and team level He aims

to utilize his strong prioritization skills, analytical ability, and team managementskills, coupled with his expertise in relation to Microsoft Power BI reportingtool, to enable a company to achieve its goals

you

If you're interested in becoming an author for Packt, please visit authors.packtpub.c

om and apply today We have worked with thousands of developers and techprofessionals, just like you, to help them share their insight with the global techcommunity You can make a general application, apply for a specific hot topicthat we are recruiting an author for, or submit your own idea

Organizing Transforming and cleansing Defining and categorizing Analysis

Visualization The Power BI ecosystem

Core, Power BI-specific Core, non-Power BI-specific Non-core, Power BI-specific Natively integrated Microsoft technologies The extended Power BI ecosystem

Power BI Pro Power BI Premium Power BI Embedded Power BI Report Server Power BI Desktop and Service

Power BI Desktop Getting data Creating a data model Analyzing data

Creating and publishing reports Power BI Service

Viewing and editing reports Creating dashboards

Sharing and collaborating with others Accessing and creating apps

Refreshing data Summary

Power BI Desktop (Report Server edition) Running Power BI Desktop

Touring the Desktop

Title Bar and Quick Access Toolbar The Ribbon

The Formula Bar Understanding DAX Views Bar

Panes Canvas Page tabs Footer Generating data

Creating a calculated table

Creating your first visualization Formatting your visualization Adding analytics to your visualization Creating and using a slicer

Creating more visualizations Editing visual interactions Summary

Touring the Power Query Editor The Title Bar and Quick Access Toolbar The Ribbon

Formula Bar The Queries Pane The Query Settings Pane Data Canvas

Footer Transforming budget and forecast data Cleaning up extraneous bottom rows Filtering rows

Unpivoting data Using Fill Changing data types Transforming people, tasks, and January data Transforming the People query

Transforming the Tasks query Transforming the January query Merging, copying, and appending queries

Merging queries Expanding tables Disabling the loading of queries Copying queries

Organizing queries Checking column quality, distribution, and profiles Loading the data

Views Bar Panes Canvas Layout Tabs Footer Modifying the layout Creating and understanding relationships Exploring the data model

Creating calculations

Calculated columns Understanding context for calculated columns Creating calculated columns for utilization Measures

Understanding context for measures Creating measures for utilization Checking and troubleshooting calculations

Boundary cases Slicing

Grouping Summary

Testing roles Using report navigation features

Drillthrough Using drillthrough Buttons

Types of buttons Button states Button actions Question and answer (Q&A) Best practices for Q&A Using a Q&A button Using Q&A in report authoring Synonyms

Bookmarks Creating and using bookmarks Advanced bookmarks

Advanced visualization techniques

Top N filtering Gauges and KPIs What-if parameters Conditional formatting Quick Measures

Report page Tooltips Creating Report page Tooltips Using Report page Tooltips Key influencers

Using a theme Creating a page template Syncing the slicers Adjusting the calendar Adding report filters Creating the final report pages

Creating the Branch Management page Creating the Hours Detail page Creating the Employee Details page Creating the Introduction page Finishing up

Testing Cleaning up Summary

Touring the Service Header

Navigation Pane Canvas

Publishing and sharing

Creating a workspace Publishing

What happens when you publish? Sharing

Personal bookmarks Persistent filters Subscribing

Copying Printing Export to PowerPoint Export to PDF

Download report Embedding reports

Secure embed codes Using URL parameters with embed codes Using the pageName parameter Using the filter parameter SharePoint Online

Publish to web Managing publish to web embed codes Editing and creating reports

Editing a report Creating a Mobile Layout Creating a report

Set as featured Phone view Ellipses menu Dashboard themes Q&A

Working with tiles Sizing and position Ellipsis menu Understanding apps

Creating an app Getting and using apps Understanding security and permissions

Workspace permissions App permissions Object permissions

Dataset permissions RLS

Personal mode Standard mode Configuring a data gateway Service Settings Diagnostics Network Connectors Managing a data gateway Gateway cluster settings and administrators Removing a gateway and adding data sources Refreshing datasets

Scheduling a refresh Summary

Questions

Further reading

Other Books You May Enjoy

To succeed in today's transforming business world, organizations need business

intelligence (BI) capabilities to make smarter decisions faster than ever before.

This Power BI book is an entry-level guide that will get you up and running withdata modeling, visualization, and analytical techniques from scratch

You'll find this book handy if you want to become well-versed with the extensiveecosystem of Power BI You'll start by covering the basics of BI and installingPower BI You'll then learn about the wide range of Power BI features to unlockbusiness insights As you progress, the book will take you through how to usePower Query to ingest, cleanse, and shape your data, and use Power BI DAX tocreate simple to complex calculations You'll also be able to add a variety ofinteractive visualizations to your reports in order to bring your data to life

Finally, you'll gain hands-on experience in creating visually stunning reports thatspeak to business decision makers, and see how you can securely share thesereports and collaborate with others

By the end of this book, you'll be ready to create simple, yet effective, BI reportsand dashboards using the latest features of Power BI

If you're an IT manager, data analyst, or BI user who is new to using Power BIfor solving BI problems, then this book is for you You'll also find this bookuseful if you want to migrate from other BI tools to create powerful and

interactive dashboards Note that no experience of working with Power BI isrequired in order to proceed

Chapter 1, Introduction to Business Intelligence and Power BI, provides an

overview of all of the various components that encompass the Power BI

ecosystem, including Power BI Desktop, Power BI Service, Power BI Licensing,Power BI Premium, data gateways, Power BI Report Server, integrations withother Microsoft technologies (such as Office 365, Flow, Visio, and PowerApps),third-party products (such as visuals, and connectors), the Power BI Community,and, if there is room, other Microsoft and third-party websites

Chapter 2, Up and Running with Power BI Desktop, shows how to download and

install the Power BI Desktop In addition to this, an overview of the major

components and interfaces of the Desktop is presented This includes the Report,Data, and Model panes; the menu tabs; and the Filters, Visualizations, and Fieldspanes Finally, we are introduced to the creation of data tables and the creation ofvisualizations

Chapter 3, Connecting and Shaping Data, serves as an introduction to the Query

Editor to import and transform data, including transposing data, creating customcolumns, adding index columns, splitting columns, referencing queries,

appending and merging queries, and other transformation functions In addition

to this, you will learn how to create data models using the relationship editor

Chapter 4, Creating Data Models and Calculations, shows us how to add

additional data to our model and create calculated measures You will use thedata and measures to create more advanced visuals, as well as explore your data

to understand the important information it contains You will also use the Q&Afeature and more advanced features to unlock insights

Chapter 5, Unlocking Insights, teaches us how to tell a story with our data by

using more advanced features, such as Bookmarks, the Selection pane, Buttons,Drillthrough, and report page tooltips In addition to this, you will learn about

"What if" parameters and much more

Chapter 6, Creating the Final Report, demonstrates how to use formatting and

other features of Power BI to take a mundane looking report and add flash, pop,

more advanced features of the Service, including how to use workspaces tocollaborate with others, how to publish their combined work as an app, and how

to find and use other apps

Chapter 10, Data Gateways and Refreshing Datasets, returns to the subject of data

by exploring how to use and manage datasets and workbooks in the Service Inaddition to this, this chapter introduces the subject of Data Gateways to assistusers in keeping their on-premises data sources up to date Finally, you will beintroduced to dataflows

A keen interest in solving BI problems will be handy Some prior experience ofusing other BI tools is also a bonus

You can download the example code files for this book from your account at www packt.com If you purchased this book elsewhere, you can visit www.packtpub.com/suppo

We also have other code bundles from our rich catalog of books and videos

available at https://github.com/PacktPublishing/ Check them out!

We also provide a PDF file that has color images of the screenshots/diagramsused in this book You can download it here: http://www.packtpub.com/sites/default/fi les/downloads/9781838644482_ColorImages.pdf

There are a number of text conventions used throughout this book

CodeInText: Indicates code words in text, database table names, folder names,filenames, file extensions, pathnames, dummy URLs, user input, and Twitterhandles Here is an example: "Download LearnPowerBI.pbix and the Budget and

Forecast.xlsx, People and Tasks.xlsx, and Hours.xlsx files from GitHub."

Warnings or important notes appear like this.

Tips and tricks appear like this.

Please leave a review Once you have read and used this book, why not leave areview on the site that you purchased it from? Potential readers can then see anduse your unbiased opinion to make purchase decisions, we at Packt can

understand what you think about our products, and our authors can see yourfeedback on their book Thank you!

For more information about Packt, please visit packt.com

The objective of this section is to introduce you to key concepts, examplescenarios, and downloading supporting data of Power BI

This section comprises of the following chapter:

Chapter 1, Introduction to Business Intelligence and Power BI

Introduction to Business Intelligence and Power BI

Power BI is a powerful ecosystem of business intelligence tools and technologies

from Microsoft But what exactly is business intelligence, anyway? Simply

stated, business intelligence is all about leveraging data in order to make better

decisions This can take many forms and is not necessarily restricted to justbusiness We use data in our personal lives to make better decisions as well Forexample, if we are remodeling a bathroom, we get multiple quotes from differentfirms The prices and details in these quotes are pieces of data that allow us tomake an informed decision in terms of which company to choose We may alsoresearch these firms online This is more data that ultimately supports our

decision

In this chapter, we will explore the key fundamental concepts of business

intelligence, as well as why business intelligence is important to organizations

In addition, we take a high-level tour of the Power BI ecosystem, licensing, andcore tools such as the Power BI Desktop and the Power BI Service

Business intelligence, in the context of organizations, revolves around making

better decisions about your business Unlike the example in the introduction,

organizations are not generally concerned with bathrooms, but rather with whatcan make their business more effective, efficient, and profitable The businesses

While business intelligence is a vast subject in and of itself, the key concepts ofbusiness intelligence can be broken down into five areas:

A domain is simply the context within which business intelligence is applied.

Most businesses are comprised of relatively standard business functions ordepartments, such as the following:

The domain helps in narrowing down the focus regarding which questions can

be answered and what decisions need to be made For example, within the

context of sales, a business might want to know which sales personnel are

performing better and which sales personnel are performing worse Businessintelligence can provide this insight as well as help determine which activitiesenable certain sales professionals to outperform others This information canthen be used to train and mentor sales personnel who are performing morepoorly

Within the context of marketing, a business can use business intelligence todetermine which types of marketing campaigns, such as email, radio, print, TV,and the web, are most effective in attracting new customers This then informsthe business where they should spend their marketing budget

Within the context of manufacturing, a business can use business intelligence to

determine the mean time between failure (MTBF) for machines that are used

in the production of goods This information can be used by the business to

Clearly, there are endless examples of where business intelligence can make anorganization more efficient, effective, and profitable Deciding on a domain inwhich to employ business intelligence techniques is a key step in enabling

business intelligence undertakings within organizations since the domain dictateswhich key questions can be answered, the possible benefits, as well as whichdata is required in order to answer those questions

Once a domain has been decided upon, the next step is identifying and acquiringthe data that's pertinent to that domain This means identifying the sources ofrelevant data These sources may be internal or external to an organization andmay be structured, unstructured, or semi-structured in nature

Internal data is data that's generated within an organization by its business

processes and operations These business processes can generate large volumes

of data that is specific to that organization's operations This data can take theform of net revenues, sales to customers, new customer acquisitions, employeeturnover, units produced, cost of raw materials, and much more time series ortransactional information This historical and current data is valuable to

organizations if they wish to identify patterns and trends, as well as for

forecasting and future planning Importantly, all the relevant data to a domainand question are almost never housed within a single data source; organizationsinevitably have multiple sources of relevant data

In addition to internal data, business intelligence is most effective when internaldata is combined with external data Crucially, external data is data that is

generated outside of the boundaries of an organization's operations Such

external data includes things such as the business's overall global economicperformance, census information, and competitor prices All of this data existsirrespective of any particular organization

Each domain and question will have internal and external data that is relevantand irrelevant to answering the question at hand However, do not be fooled intobelieving that simply because you have chosen manufacturing/production as thedomain that other domains such as sales and marketing do not have relevantsources of data If you are trying to forecast the required production levels, salesdata in terms of pipelines can be very relevant Similarly, external data thatpoints toward overall economic growth may also be extremely relevant whiledata such as the cost of raw materials may very well be irrelevant

structured data

Structured, unstructured, and semi-Structured data is data that conforms to a rather formal specification of tableswith rows and columns Think of a spreadsheet where you might have columnsfor the transaction ID, customer, units purchased, and price per unit Each rowrepresents a sales transaction Structured data sources are the easiest sources forbusiness intelligence tools to consume and analyze These sources are most oftenrelational databases, which include technologies such as Microsoft SQL Server,Microsoft Access, Azure Table storage, Azure SQL database, Oracle, MySQL,IBM DB2, Teradata, PostgreSQL, Informix, and Sybase In addition, this

category of data sources includes relational database standards and APIs such as

Open Database Connectivity (ODBC) and Object Linking and Embedding Database (OLE DB).

Unstructured data is effectively the opposite of structured data Unstructureddata cannot be organized into simple tables with rows and columns Such dataincludes things such as videos, audio, images, and text Word processing

documents, emails, social media posts, and web pages are also examples oflargely unstructured data Unstructured data sources are the most difficult types

of sources for business intelligence tools to consume and analyze This type ofdata is either stored as binary large objects (BLOBS) or as a file in a filesystem

such as the New Technology File System (NTFS) or the Hadoop Distributed

File System (HDFS).

Unstructured data also includes so-called NoSQL databases, which include datastores such as document databases, graph databases, and key-value stores Thesedatabases are specifically designed to store unstructured data Document

databases include Microsoft Azure Cosmos DB, MongoDB, 10Gen, Cloudant(IBM), Couchbase, and MarkLogic Graph databases include Neo4j and

HyperGraphDB Key-value stores include Microsoft's Cosmos DB, Basho

Technologies' Riak, Redis, Aerospike, Amazon Web Services' DynamoDB,Basho Technologies, Couchbase, Datastax's Cassandra, MapR Technologies, andOracle Finally, wide-column stores include Cassandra and HBase

definition of structured data, that is, tables with rows and columns Examples ofsemi-structured include tab and delimited text files, XML, other markup

The vast majority of business intelligence tools, such as Power BI, are optimizedfor handling structured and semi-structured data Structured data sources

integrate natively with how business intelligence tools are designed In addition,business intelligence tools are designed to ingest semi-structured data sourcesand transform them into structured data Unstructured data is more difficult butnot impossible to analyze with business intelligence tools In fact, Power BI has

a number of features that are designed to ease the ingestion and analysis of

unstructured data sources However, analyzing such unstructured data has itslimitations

A model, or data model, refers to the way in which one or more data sources are

organized in order to support analysis and visualization Models are built bytransforming and cleansing data, helping to define the types of data within thosesources, as well as the definition of data categories for specific data types

Models can be extremely simple, such as a single table with columns and rows.However, business intelligence almost always involves multiple tables of data,and most often involves multiple tables of data coming from multiple sources.Thus, the model becomes more complex as the various sources and tables of datamust be combined into a cohesive whole This is done by defining how each ofthe disparate sources of data relates to one another As an example, let's say youhave one data source that represents a customer's name, contact information, andperhaps size in revenue and/or the number of employees This information might

come from an organization's customer relationship management (CRM)

system The second source of data might be order information, which includesthe customer's name, units purchased, and the price that was paid This second

source of data comes from the organization's enterprise resource planning (ERP) system These two sources of data can be related to one another based on

the name of the customer

Some sources of data have prebuilt models This includes traditional data

warehouse technologies for structured data as well as analogous systems forperforming analytics over unstructured data The traditional data warehouse

technology is generally built upon the online analytical processing (OLAP) technology and includes systems such as Microsoft's SQL Server Analysis

Services (SSAS), Azure Analysis Services, Snowflake, Oracle's Essbase,

AtScale cubes, SAP HANA and Business Warehouse servers, and Azure SQLData Warehouse With respect to unstructured data analysis, technologies such asApache Spark, Databricks, and Azure Data Lake Storage are used

When building a data model, it is often (read always) necessary to clean and

transform the source data Data is never clean—it must always be massaged in

order for bad data to be removed or resolved For example, when dealing withcustomer data from a CRM system, it is not uncommon to have the same

customer entered into the system with multiple spellings The format of data inspreadsheets may make data entry easy for humans but can be unsuitable forbusiness intelligence purposes In addition, data may have errors, missing data,inconsistent formatting, or even have something as seemingly simple as trailingspaces All of these types of situations can cause problems when performingbusiness intelligence analysis Luckily, business intelligence tools such as Power

BI provide mechanisms for cleansing and reshaping the data to support analysis.This might involve replacing or removing errors in the data, pivoting,

unpivoting, or transposing rows and columns, removing trailing spaces, or othertypes of transformation operations

Transforming and cleansing technologies are often referred to as extract,

transform, load (ETL) tools and include products such as Microsoft's SQL Server Integration Services (SSIS), Azure Data Factory, Alteryx, Informatica,

Dell Boomi, Salesforce's Mulesoft, Skyvia, IBM's Infosphere Information

Server, Oracle Data Integrator, Talend, Pentaho Data Integration, SAS's DataIntegration Studio, Sybase ETL, and QlikView Expressor