You're reading from Expert Data Modeling with Power BI - Second Edition

Product type Book

Published in Apr 2023

Publisher Packt

ISBN-13 9781803246246

Pages 698 pages

Edition 2nd Edition

Languages

Concepts

Business Intelligence

Author (1):

Soheil Bakhshi

Table of Contents (22) Chapters

Preface

1. Section I: Data Modeling in Power BI

2. Introduction to Data Modeling in Power BI

3. Data Analysis eXpressions and Data Modeling

4. Section II: Data Preparation in Query Editor

5. Data Preparation in Power Query Editor

6. Getting Data from Various Sources

7. Common Data Preparation Steps

8. Star Schema Preparation in Power Query Editor

9. Data Preparation Common Best Practices

10. Section III: Data Modeling

11. Data Modeling Components

12. Star Schema and Data Modeling Common Best Practices

13. Section IV: Advanced Data Modeling

14. Advanced Data Modeling Techniques

15. Row-Level and Object-Level Security

16. Dealing with More Advanced Data Warehousing Concepts in Power BI

17. Introduction to Dataflows

18. DirectQuery Connections to Power BI Datasets and Analysis Services in Composite Models

19. New Options, Features, and DAX Functions

20. Other Books You May Enjoy

21. Index

Use query parameters

As we discussed in Chapter 3, Data Preparation in Power Query Editor, in the Understanding query parameters section, query parameters are one of the most valuable features of Power Query that can help us in many scenarios. The following sections explain some of them.

Parameterizing connections

It is best to parameterize all data source connections, especially if we have different environments. The parameterized connections become handy when configuring the deployment pipelines.

Restricting the row counts in development for large tables

Sometimes, we deal with large tables with several million rows, which can be time-consuming in development. Imagine a scenario in which we transform the data in Power Query Editor and then load it into the data model. If the table in the source system is large, it takes a long time to load the data into the data model, decreasing our productivity. In most cases, we do not need to load all the data into the data model...