You're reading from Microsoft Power BI Performance Best Practices Learn practical techniques for building high-speed Power BI solutions

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835082256

Length 346 pages

Edition 2nd Edition

Languages

DAX

Tools

Azure

Concepts

Business Intelligence

Authors (2):

Thomas LeBlanc

Bhavik Merchant

View More author details

Table of Contents (23) Chapters

Preface

1. Part 1: Architecture, Bottlenecks, and Performance Targets FREE CHAPTER

2. Chapter 1: Setting Targets and Identifying Problem Areas

3. Chapter 2: Exploring Power BI Architecture and Configuration

4. Chapter 3: Learning the Tools for Performance Tuning

5. Part 2: Performance Analysis, Improvement, and Management

6. Chapter 4: Analyzing Logs and Metrics

7. Chapter 5: Optimization for Storage Modes

8. Chapter 6: Third-Party Utilities

9. Chapter 7: Performance Governance Framework

10. Part 3: Fetching, Transforming, and Visualizing Data

11. Chapter 8: Loading, Transforming, and Refreshing Data

12. Chapter 9: Report and Dashboard Design

13. Part 4: Data Models, Calculations, and Large Semantic Models

14. Chapter 10: Dimensional Modeling and Row Level Security

15. Chapter 11: Improving DAX

16. Chapter 12: High Scale Patterns

17. Part 5: Optimizing Capacities in Power BI Enterprises

18. Chapter 13: Working with Capacities

19. Chapter 14: Performance Needs for Fabric Artifacts

20. Chapter 15: Embedding in Web Apps

21. Index

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Summary

In this chapter, we began to dive deeper into specific areas of an actual Power BI solution, starting from transforming and loading data. We saw how Power Query and the mashup engine take center stage in this part of the pipeline, powered by the M query language. Additionally, we learned about parallelism and how you can change the settings to improve performance. There are settings that can be adjusted to speed up the developer experience and optimize data loading.

We moved on to transformations, focusing on operations that can slow down with large volumes of data such as filtering and joining. We introduced the mashup engine’s ability to perform query folding and why we should leverage this because it pushes resource-intensive operations down to the data source. We learned how to see where folding is occurring in Power BI Desktop and examined how to configure incremental refresh to reduce the amount of data loaded.

The Power Query diagnostic logs contain information...