Book Image

Extreme DAX

By : Michiel Rozema, Henk Vlootman
Book Image

Extreme DAX

By: Michiel Rozema, Henk Vlootman

Overview of this book

This book helps business analysts generate powerful and sophisticated analyses from their data using DAX and get the most out of Microsoft Business Intelligence tools. Extreme DAX will first teach you the principles of business intelligence, good model design, and how DAX fits into it all. Then, you’ll launch into detailed examples of DAX in real-world business scenarios such as inventory calculations, forecasting, intercompany business, and data security. At each step, senior DAX experts will walk you through the subtleties involved in working with Power BI models and common mistakes to look out for as you build advanced data aggregations. You’ll deepen your understanding of DAX functions, filters, and measures, and how and when they can be used to derive effective insights. You’ll also be provided with PBIX files for each chapter, so that you can follow along and explore in your own time.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Part I: Introduction
Part I: Introduction
2
1.1 DAX in Business Intelligence
1.1 DAX in Business Intelligence
The Five-Layer model for business intelligence
Enterprise BI and end-user BI
Where DAX fits in, and where to find it
Tools to develop models and DAX
Powered by DAX: visual, interactive reports
How to approach solution development
The digital transformation cycle
Summary
3
1.2 Model Design
1.2 Model Design
Columnar data storage
Data types and encoding
Relationships
Effective model design
Memory and performance considerations
Summary
4
1.3 Using DAX
1.3 Using DAX
Calculated columns
Calculated tables
Measures
DAX security filters
DAX queries
Date tables
Best practices in DAX
Summary
5
1.4 Context and Filtering
1.4 Context and Filtering
The Power BI model
Introduction to DAX context
DAX filtering: Using CALCULATE
Time intelligence
Changing relationship behavior
Table functions in DAX
Filtering with table functions
DAX variables
Summary
6
Part II: Business cases
Part II: Business cases
7
2.1 Security with DAX
2.1 Security with DAX
Introduction to row-level security (RLS)
Securing hierarchies using PATH functions
Securing attributes
Securing aggregation levels
Summary
8
2.2 Dynamically Changing Visualizations
2.2 Dynamically Changing Visualizations
The business case
Dynamic measures
Dynamic labels
Combining dynamic labels and dynamic calculations
Summary
9
2.3 Alternative Calendars
2.3 Alternative Calendars
Week-based and Gregorian calendars
Creating a week-based calendar table
Time Intelligence calculations for week-based calendars
Keeping your report current
Summary
10
2.4 Working with AutoExist
2.4 Working with AutoExist
The Power BI model
How Power BI visualizes the output of a model
What AutoExist is, and what it does
Example: The case of the missing workdays
How to solve the missing workdays problem
Optimizing report performance with AutoExist
Summary
11
2.5 Intercompany Business
2.5 Intercompany Business
Modeling the QuantoBikes sales process
Business between subsidiaries
Future sales
Summary
12
2.6 Exploring the Future: Forecasting and Future Values
2.6 Exploring the Future: Forecasting and Future Values
Financial calculations
Financial DAX functions
The business case and model
Calculating Future Value (FV)
Calculating Net Present Value (NPV)
Calculating the Internal Rate of Return (IRR)
Calculating cost-covering rent
Summary
13
2.7 Inventory Analysis
2.7 Inventory Analysis
Data modeling for status-oriented data
Basic inventory calculations
Inventory forecasting
Summary
14
2.8 Personnel Planning
2.8 Personnel Planning
The Power BI model
Calculating sales
Calculating FTEs needed
Optimizing the Power BI model
Considering aggregation levels
Summary
15
Other Books You May Enjoy
Other Books You May Enjoy
16
Index
Index

Effective model design

The concepts of relationships and filter propagation enable powerful analytics in Power BI models. It is important to think about the design of a model: which tables should the model contain, and which columns need to be in those tables? Which relationships are needed? In short, what is the overall structure of the model? The choices you make in model design will determine what results the model will be able to deliver.

Star schemas and snowflakes

A best practice for analytics using relational databases is to work with a specific database structure, known as a star schema. The basic ideas of star schemas apply to Power BI models as well.

Figure 1.2.12: Generic star schema structure

The central tables in a star schema model are the fact tables. These tables contain things that have happened, will happen, or should happen; like sales transactions, financial ledger transactions, customer inquiries, student enrollments, sales opportunities...

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