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Data Cleaning with Power BI

You're reading from  Data Cleaning with Power BI

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781805126409
Pages 340 pages
Edition 1st Edition
Languages
Author (1):
Gus Frazer Gus Frazer
Profile icon Gus Frazer

Table of Contents (23) Chapters

Preface 1. Part 1 – Introduction and Fundamentals
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

Handling large and complex datasets

Power BI is an excellent tool for data visualization and analysis, but what happens when you’re dealing with big data?

When datasets become massive, challenges arise in terms of performance, data modeling, and query optimization.

In this section, we’ll explore best practices for handling big data within Power BI, ensuring that you can still unlock valuable insights without compromising performance.

Understanding big data

Big data typically refers to datasets that are too large or complex for traditional data processing applications. These datasets often exceed the capacity of conventional software and may include various data types, such as structured, semi-structured, or unstructured data.

Challenges of working with big data in Power BI

When dealing with big data in Power BI, several challenges emerge:

  • Performance: Large datasets can slow down report generation and visualization. Users expect responsive dashboards...
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