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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

Summary

In this chapter, we introduced a repeatable process to help you manage performance pro-actively in your organization. This is important for consistency and the overall satisfaction of users. If we catch and repair issues before they become widespread, we can save time and money. We started that conversation with baselines for various artifacts and factors for baselines.

Baselines lead to maintaining performance history for trends and spotting anomalies. When problematic content is identified, the recommendation for remediation work is prioritized based on business value and use impact. That investment involves metrics and tools we described in previous chapters to profile systems and slow areas. We then learned about taking lessons from any fixes back into standards and common practices to reduce future issues.

The chapter then transitioned to ways to share knowledge and awareness of performance issues with the developer community and how to leverage guidance documentation...

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