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Hands-On Business Intelligence with DAX

Hands-On Business Intelligence with DAX

By : Horne
5 (3)
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Hands-On Business Intelligence with DAX

Hands-On Business Intelligence with DAX

5 (3)
By: Horne

Overview of this book

Data Analysis Expressions (DAX) is known for its ability to increase efficiency by extracting new information from data that is already present in your model. With this book, you’ll learn to use DAX’s functionality and flexibility in the BI and data analytics domains. You’ll start by learning the basics of DAX, along with understanding the importance of good data models, and how to write efficient DAX formulas by using variables and formatting styles. You’ll then explore how DAX queries work with the help of examples. The book will guide you through optimizing the BI workflow by writing powerful DAX queries. Next, you’ll learn to manipulate and load data of varying complexity within Microsoft products such as Power BI, SQL Server, and Excel Power Pivot. You’ll then discover how to build and extend your data models to gain additional insights, before covering progressive DAX syntax and functions to understand complex relationships in DAX. Later, you’ll focus on important DAX functions, specifically those related to tables, date and time, filtering, and statistics. Finally, you’ll delve into advanced topics such as how the formula and storage engines work to optimize queries. By the end of this book, you’ll have gained hands-on experience in employing DAX to enhance your data models by extracting new information and gaining deeper insights.
Table of Contents (18 chapters)
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1
Section 1: Introduction to DAX for the BI Pro
7
Section 2: Understanding DAX Functions and Syntax
14
Section 3: Taking DAX to the Next Level

Simplifying your data model

In Chapter 3, Building Data Models, we looked at the process of building a data model in some detail. In this section, we will revisit some of the topics we covered, along with what we have just learned about the workings of the VertiPaq engine. Using this combined knowledge, we will focus on some of the ways in which we can optimize the overall memory requirements of our data models and improve performance.

As we saw in the previous section, VertiPaq's ability to compress data is dependent on several factors. As such, there are some elements that you need to consider when building a data model that will have a direct impact on the efficiency of the compression algorithms. These include the following:

  • The cardinality of values in a column. This affects VertiPaq's ability to reduce the number of bits used to store a value. If a column has...

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