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

Columnar data storage

The power of the Power BI model comes primarily from a smart data storage mechanism. Power BI models are, in fact, databases in the sense that they organize and store data. But the internals are very different from other database technologies you may be familiar with.

Relational databases

The traditional, corporate way of working with data is with a relational database management system (RDBMS) like Microsoft SQL Server. In an RDBMS, tables of data are defined with a fixed number of data columns per table. Each column must have a data type, like integer, text, or decimal number, and from this information, the RDBMS can derive how much space is needed to store a single row of data, or record, and how many rows can be stored in a disk-based data file. This concept makes an RDBMS an effective choice for applications that process transactions, like sales transactions from a web shop or transactions in a company's financial ledger.

The concept of...

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