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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 11: Improving DAX

In the previous chapter, we focused on Import datasets at the visual layer in Power BI, where a key point was to reduce the load on data sources by minimizing the complexity and number of queries that are issued to the Power BI dataset.

In theory, a well-designed data model should not experience performance issues easily unless there are extremely high data volumes with tens of millions of rows or more. However, it is still possible to get poor performance with good data models due to the way DAX measures are constructed.

Learning DAX basics is considered quite easy by many people. It can be approached by people without a technical data background but who are comfortable writing formulas in a tool such as Microsoft Excel. However, mastering DAX can be challenging. This is because DAX is a rich language with multiple ways to achieve the same result. Mastery requires having knowledge of row context and filter context, which determines what data is in...

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