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.
Table of Contents (17 chapters)
Free Chapter
1
Part I: Introduction
6
Part II: Business cases
15
Other Books You May Enjoy
16
Index

Measures

Measures, or in some earlier model versions, calculated fields, are without a doubt the most powerful element of Power BI models. In fact, most of the work we do on Power BI models comes down to designing and implementing DAX measures.

When using a numeric column from a fact table in a Power BI report, the column values will be aggregated. The basic aggregations available are sum, average, minimum, maximum, count, distinct count, and some statistical aggregations like standard deviation, variance, and median. The basic aggregations differ depending on the data type; for a Date column, for instance, you can choose earliest and latest, count, and distinct count only. When you use columns this way, the Power BI model creates an implicit measure in the background: an aggregation function that returns the selected aggregation of the values in the column.

In real life, many of the required insights come down to aggregations that cannot be expressed in terms of basic aggregations...