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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

Scaling with composite models and aggregations

So far, we have discussed how Import mode offers the best possible speed for Power BI datasets. However, sometimes, high data volumes and their associated refresh limitations may lead you to select DirectQuery mode instead. At this point, you may want to review the Choosing between Import and DirectQuery mode section on choosing a storage mode in Chapter 2, Exploring Power BI Architecture and Configuration, to remind yourself about the differences and rationale for choosing one over the other.

We also discussed how the Analysis Services engine is designed to aggregate data efficiently because BI solutions typically aggregate data most of the time. When we use DirectQuery, we want to push these aggregations down to the source where possible to avoid Power BI having to bring all the data over to compute them. With very large tables containing tens of millions to billions of rows, these aggregations can be costly and time-consuming, even...

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