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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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Toc

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

Summary

In this chapter, we learned how to deal with exceptionally large volumes of data. The first use case was where we had Power BI datasets growing beyond the 1 GB storage limit that's available to Power BI Pro users in the Shared capacity. In such cases, we recommended considering Power BI Premium. The dataset limit in Premium is 10 GB. With the large dataset storage format enabled, we learned that datasets could grow well beyond this size. Technically, we can use all the available memory on the capacity, which is 400 GB on a Premium P5 capacity. Larger Premium capacities also have higher concurrency limits, which can give us better refresh and query performance.

Then, we looked at a case where the scale problem comes from concurrent users and learned why this can put pressure on memory and CPU resources. We introduced AAS as a solution to this problem due to its ability to leverage QSO. We also recommended using partitions on Premium and AAS to speed up refreshes on...

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