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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

Understanding Premium services, resource usage, and Autoscale

Power BI Premium provides reserved capacity for your organization. This isolates you from the noisy-neighbor problem that you may experience in the shared capacity. Let's start by briefly reviewing the capabilities of the Premium capacities that differentiate it by providing greater performance and scale:

  • Ability to Autoscale: This is a new capability that was introduced in Gen2 that allows administrators to assign spare CPU cores to be used in periods of excessive load (not available for PPU).
  • Higher Storage and Dataset Size Limits: 100 TB of total storage and a 400 GB dataset size (100 GB in PPU).
  • More Frequent Dataset Refresh: 48 times per day via the UI and potentially more via scripting through the XMLA endpoint.
  • Greater Refresh Parallelism: You can have more refreshes running at the same time, ranging from 5 (Embedded A1) to 640 (Premium P5/Embedded A8).
  • Advanced Dataflows Features: Premium...
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