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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

Chapter 12: High-Scale Patterns

In the previous chapter, we learned how to optimize DAX expressions. Having reached this point, we have rounded up all the advice on optimizing different layers of a Power BI solution, from the dataset layer to report design. In this chapter, we will take a step back and revisit architectural concepts and related features that help deal with very high data volumes.

The amount of data that organizations collect and need to analyze is increasing all the time. With the advent of the Internet of Things (IoT) and predictive analytics, certain industries, such as energy and resources, are collecting more data than ever. It is common for a modern mine or gas plant to have tens of thousands of sensors, each generating many data points at granularities much higher than a second.

Even with Power BI's data compression technology, it isn't always possible to load and store massive amounts of data in an Import mode model in a reasonable amount of...

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