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

Length: 387 pages
Edition: 1
Language: English
Publisher: Apress
Publication Date: 2022-06-24
ISBN-10: 148428187X
ISBN-13: 9781484281871
Sales Rank: #0 (See Top 100 Books)

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 the same 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

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

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