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Data Cleaning with Power BI

You're reading from   Data Cleaning with Power BI The definitive guide to transforming dirty data into actionable insights

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Product type Paperback
Published in Feb 2024
Publisher
ISBN-13 9781805126409
Length 340 pages
Edition 1st Edition
Languages
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Author (1):
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Gus Frazer Gus Frazer
Author Profile Icon Gus Frazer
Gus Frazer
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Table of Contents (23) Chapters Close

Preface 1. Part 1 – Introduction and Fundamentals FREE CHAPTER
2. Chapter 1: Introduction to Power BI Data Cleaning 3. Chapter 2: Understanding Data Quality and Why Data Cleaning is Important 4. Chapter 3: Data Cleaning Fundamentals and Principles 5. Chapter 4: The Most Common Data Cleaning Operations 6. Part 2 – Data Import and Query Editor
7. Chapter 5: Importing Data into Power BI 8. Chapter 6: Cleaning Data with Query Editor 9. Chapter 7: Transforming Data with the M Language 10. Chapter 8: Using Data Profiling for Exploratory Data Analysis (EDA) 11. Part 3 – Advanced Data Cleaning and Optimizations
12. Chapter 9: Advanced Data Cleaning Techniques 13. Chapter 10: Creating Custom Functions in Power Query 14. Chapter 11: M Query Optimization 15. Chapter 12: Data Modeling and Managing Relationships 16. Part 4 – Paginated Reports, Automations, and OpenAI
17. Chapter 13: Preparing Data for Paginated Reporting 18. Chapter 14: Automating Data Cleaning Tasks with Power Automate 19. Chapter 15: Making Life Easier with OpenAI 20. Assessments 21. Index 22. Other Books You May Enjoy

Summary

In this chapter, we delved into the intricate world of data modeling and managing relationships within Power BI. It provided a brief overview of and introduction to the pivotal role well-structured data modeling plays in ensuring clean and reliable data for informed decision-making.

We started by exploring/recapping the basics of dimension modeling in Power BI, bidirectional cross-filtering, understanding its power, identifying potential errors and bottlenecks, and adopting best practices to use it effectively. We also comprehensively covered the concept of cardinality with an emphasis on its impact on data cleanliness and performance.

Later, we learned how to make the right choices to create accurate and high-performing data models, with insights into challenges and best practices for optimizing performance and managing vast data. Lastly, we explored the complexities of avoiding circular references, gaining strategies and best practices to ensure data clarity.

In...

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