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Microsoft Power BI Performance Best Practices

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

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835082256
Length 346 pages
Edition 2nd Edition
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Authors (2):
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Thomas LeBlanc Thomas LeBlanc
Author Profile Icon Thomas LeBlanc
Thomas LeBlanc
Bhavik Merchant Bhavik Merchant
Author Profile Icon Bhavik Merchant
Bhavik Merchant
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Toc

Table of Contents (23) Chapters Close

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 22. Other Books You May Enjoy

Dimensional Modeling and Row Level Security

In the previous chapter, we looked at the visual layer in Power BI, where a key point was to reduce the load on data sources by minimizing the report page complexity and number of queries sent to the Analysis Services engine. We learned that this area is usually the easiest and quickest place to apply performance-related fixes. However, experience working with a wide range of Power BI solutions has shown that issues with the underlying semantic model are very common and typically have a greater negative performance impact. Importantly, this impact can be amplified because a semantic model can be used by more than one report. Semantic model reuse is a recommended best practice to reduce data duplication and development effort.

Therefore, in this chapter, we will move one layer deeper, into Power BI semantic models with a focus on Import mode. Semantic model design is arguably the most critical piece, being at the core of a Power BI solution...

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