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

Introduction to Power Query features for data modelers

This section looks at some features currently available within Power Query Editor that help data modelers identify and fix errors quickly. Data modelers can understand data quality, statistics, and data distribution within a column (not the overall dataset). For instance, a data modeler can quickly see a column’s cardinality, how many empty values a column has, and so forth.

As previously mentioned, the information provided by the Column quality, Column distribution, and Column profile features is calculated based on the top 1000 rows of data (by default), which sometimes leads to false information. It is good practice to set Column profile to get calculated based on the entire dataset for smaller amounts of data. However, this approach may take a while to load the column profiling information for larger amounts of data, so be careful while changing this setting if you are dealing with large tables.

To change the...