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Microsoft Power BI Performance Best Practices

You're reading from   Microsoft Power BI Performance Best Practices A comprehensive guide to building consistently fast Power BI solutions

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801076449
Length 312 pages
Edition 1st Edition
Languages
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Author (1):
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Bhavik Merchant Bhavik Merchant
Author Profile Icon Bhavik Merchant
Bhavik Merchant
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Architecture, Bottlenecks, and Performance Targets
2. Chapter 1: Setting Targets and Identifying Problem Areas FREE CHAPTER 3. Chapter 2: Exploring Power BI Architecture and Configuration 4. Chapter 3: DirectQuery Optimization 5. Part 2: Performance Analysis, Improvement, and Management
6. Chapter 4: Analyzing Logs and Metrics 7. Chapter 5: Desktop Performance Analyzer 8. Chapter 6: Third-Party Utilities 9. Chapter 7: Governing with a Performance 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 Datasets
14. Chapter 10: Data Modeling and Row-Level Security 15. Chapter 11: Improving DAX 16. Chapter 12: High-Scale Patterns 17. Part 5: Optimizing Premium and Embedded Capacities
18. Chapter 13: Optimizing Premium and Embedded Capacities 19. Chapter 14: Embedding in Applications 20. Other Books You May Enjoy

Spotting and mitigating performance issues

First, we will cover some recommended practices in working with Performance Analyzer to ensure you are comparing the same thing each time you test. This is important, so you should try to eliminate as many variables as possible and simulate the same conditions, whatever you can reasonably control.

Achieving consistency in tests

When you have a .pbix file open in Power BI Desktop, the dataset has already been loaded into memory. For import models, the file could be quite large, easily a few gigabytes (GB). You are likely to have noticed that Power BI Desktop takes longer to start up when opening a very large file. Much of this time is taken by the dataset being loaded from disk into memory. This concept applies in the Power BI service too, after you deploy datasets there.

The Power BI service does not keep all datasets in memory all the time. The service applies some heuristics to decide when to free up memory. If you haven&apos...

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