Up and Running with DAX for Power BI: A Concise Guide for Non-Technical Users

✍ Scribed by Alison Box

• Publisher: Apress
• Year: 2022
• Tongue: English
• Leaves: 376
• Category: Library

✦ Synopsis

Take a concise approach to learning how DAX, the function language of Power BI and PowerPivot, works. This book focuses on explaining the core concepts of DAX so that ordinary folks can gain the skills required to tackle complex data analysis problems. But make no mistake, this is in no way an introductory book on DAX. A number of the topics you will learn, such as the concepts of context transition and table expansion, are considered advanced and challenging areas of DAX.

While there are numerous resources on DAX, most are written with developers in mind, making learning DAX appear an overwhelming challenge, especially for those who are coming from an Excel background or with limited coding experience. The reality is, to hit the ground running with DAX, it’s not necessary to wade through copious pages on rarified DAX functions and the technical aspects of the language. There are just a few mandatory concepts that must be fully understood before DAX can be mastered. Knowledge of everything else in DAX is built on top of these mandatory aspects.

Author Alison Box has been teaching and working with DAX for over eight years, starting with DAX for PowerPivot, the Excel add-in, before moving into the Power BI platform. The guide you hold in your hands is an outcome of these years of experience explaining difficult concepts in a way that people can understand. Over the years she has refined her approach, distilling down the truth of DAX which is “you can take people through as many functions as you like, but it’s to no avail if they don’t truly understand how it all works.”

You will learn to use DAX to gain powerful insights into your data by generating complex and challenging business intelligence calculations including, but not limited to:

• Calculations to control the filtering of information to gain better insight into the data that matters to you
• Calculations across dates such as comparing data for thesame period last year or the previous period
• Finding rolling averages and rolling totals
• Comparing data against targets and KPIs or against average and maximum values
• Using basket analysis, such as “of customers who bought product X who also bought product Y”
• Using “what if” analysis and scenarios
• Finding “like for like” sales
• Dynamically showing TopN/BottomN percent of customers or products by sales
• Finding new and returning customers or sales regions in each month or each year

Who This Book Is For

Excel users and non-technical users of varying levels of ability or anyone who wants to learn DAX for Power BI but lacks the confidence to do so

✦ Table of Contents

Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Show Me the Data
Stars and Snowflakes
Fact Tables
Dimensions
The Star Schema
Finding Nonmatching Values
Chapter 2: DAX Objects, Syntax, and Formatting
DAX Syntax
DAX Formatting
Chapter 3: Calculated Columns and Measures
Calculated Columns
Creating Simple Calculated Columns
Looking at the RELATED Function
DAX Measures
Implicit Measures
Explicit Measures
Creating a Measures Table
Creating Simple DAX Measures
What Exactly Is a Measure?
All Report Visuals Use Measures
Measures Return Scalar Values
Chapter 4: Evaluation Context
The Filter Context
Evaluations Using a Single Filter
Calculation in the Total Row
Evaluations Using Multiple Filters
The Row Context
Chapter 5: Iterators
The SUMX Function (and Other “X” Functions)
Total Row Grief
Chapter 6: The CALCULATE Function
Why You Need CALCULATE
Using Single Filters
Using Multiple Filters
AND and OR Filters
Complex Filters
Chapter 7: DAX Table Functions
Types of DAX Functions
Table Functions
Examples of Table Expressions
Why Do We Need Table Expressions?
The FILTER Function
FILTER Used to Reduce Rows
FILTER as the Filter Argument of CALCULATE
Column Filters vs. Table Filters
Table Filters Are Less Efficient
Table Filters Return Different Results
Using the KEEPFILTERS Function
Chapter 8: The ALL Function and All Its Variations
The ALL Function
Applied to the Fact Table
Using ALL on Dimension Tables
Using ALL on a Column
The ALLEXCEPT Function
The ALLSELECTED Function
ALL as a Modifier to CALCULATE
Chapter 9: Calculations on Dates: Using DAX Time Intelligence
Power BI Date Hierarchies
Creating a Date Table
Using Time Intelligence Functions
Previous Month/Year – PREVIOUSMONTH/YEAR
Same Period Last Year – SAMEPERIODLASTYEAR
Values for Any Time Ago – DATEADD
Year to Date – DATESYTD
Total to Date or Cumulative Totals
Rolling Annual Totals and Averages
Calculating the Last Transaction Date and the Last Transaction Value
Finding the Difference Between Two Dates
Chapter 10: Empty Values vs. Zero
The BLANK() Function
The ISBLANK Function
Testing for Zero
Using Measures to Find Blanks and Zero
Using the COALESCE Function
Chapter 11: Using Variables: Making Our Code More Readable
Improved Performance
Improved Readability
Reduced Complexity
Variables As Constants
Chapter 12: Returning Values in the Current Filter
The SELECTEDVALUE Function
The CONCATENATEX Function
Using Parameter Tables
The Values Function
A Table or a Scalar Function?
Replacing “Lost Filters”
Converting Columns to Tables
Chapter 13: Controlling the Direction of Filter Propagation
Programming Bidirectional Filters
Why You Should Never Use Bidirectional Relationships
Chapter 14: Working with Multiple Relationships Between Tables
Activating Inactive Relationships
Comparing Values in the Same Column
Chapter 15: Understanding Context Transition
Overview of DAX Evaluations Contexts
Row Context Revisited
Filter Context Revisited
How Row Context Becomes Filter Context
How Context Transition Can Return “Surprising Results”
Filters Using AVERAGE
Filters Using MAX
Filters Using Measures
Aggregating Totals Using Context Transition
Aggregating in Dimensions
Aggregating in Virtual Tables
Using ALL to Group Columns in the Same Table
Using SUMMARIZE to Group Columns from Related Tables
Chapter 16: Leveraging Context Transition
Ranking Data: Looking at RANKX
Binning Measures into Numeric Ranges
Calculating TopN Percent
Create the Slicers
Create the Measure to Find the Top or Bottom Percent Selected in Slicers
Calculating “Like for Like” Yearly Sales Using SUMMARIZE
Using Context Transition in Calculated Columns
Calculating Running Totals
Calculating the Difference from the Value in the Previous Row
Chapter 17: Virtual Relationships: The LOOKUPVALUE and TREATAS Functions
LOOKUPVALUE Function
The TREATAS Function
Chapter 18: Table Expansion
Revisiting Filters
Column Filters Revisited
The ALL Function Revisited
Expanded Tables Explained
Leveraging Expanded Tables
“Reaching” Dimensions
Table Expansion vs. CROSSFILTER
Using Snowflake Schemas
Chapter 19: The CALCULATETABLE Function
CALCULATETABLE vs. FILTER
CALCULATETABLE and Table Expansion
Calculating “New” Entities
Calculating “Returning” Entities
Index