Book Image

Expert Data Modeling with Power BI - Second Edition

By : Soheil Bakhshi
4 (1)
Book Image

Expert Data Modeling with Power BI - Second Edition

4 (1)
By: Soheil Bakhshi

Overview of this book

This book is a comprehensive guide to understanding the ins and outs of data modeling and how to create full-fledged data models using Power BI confidently. In this new, fully updated edition, you'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization. As you advance through the chapters, the book will demonstrate how to prepare efficient data models in the Power Query Editor and use simpler DAX code with new data modeling features. You'll explore how to use the various data modeling and navigation techniques and perform custom calculations using the modeling features with the help of real-world examples. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks. Additionally, you'll learn valuable best practices and explore common data modeling complications and the solutions to supercharge the process of creating a data model in Power BI and build better-performing data models. By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support high-performing reports and data analytics.
Table of Contents (22 chapters)
1
Section I: Data Modeling in Power BI
4
Section II: Data Preparation in Query Editor
10
Section III: Data Modeling
13
Section IV: Advanced Data Modeling
20
Other Books You May Enjoy
21
Index

Using calculation groups

Creating calculation groups is one of the most useful features for Power BI data modelers and developers. It reduces the number of measures you have to create. Calculation groups address the fact that we have to create many measures in larger and more complex data models that are somewhat redundant. Creating those measures takes a lot of development time. For instance, in a Sales data model, we can have Sales Amount as a base measure. In real-world scenarios, we usually have to create many time intelligence measures on top of the Sales Amount measure, such as Sales Amount YTD, Sales Amount QTD, Sales Amount MTD, Sales Amount LYTD, Sales Amount LQTD, Sales Amount LMTD, and so on. We have seen models with more than 20 time intelligence measures created on top of a single measure. In real-world scenarios, we have far more base measures than a business that requires all those 20 time intelligence measures for every single base measure. You can imagine how time...