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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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Tools
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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

Setting Targets and Identifying Problem Areas

Many people would consider report performance as the most critical area to focus on when trying to improve the speed of an analytics solution. This is largely true, because it is the most visible part of the system used by pretty much every class of user, from administrators to business executives. However, you will learn that there are other areas of the solution that should be considered if performance is to be managed comprehensively. For example, achieving good performance in the reporting layer might be of no consequence if the underlying dataset that powers the report takes a long time to be refreshed or is susceptible to failures due to resource limits or system limits being reached. In this case, users may have great-looking, fast reports that do not provide value because the data is stale.

The authors of this book have experienced the effects of poor report performance firsthand but have also enjoyed the fruits of improving...

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