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Data Cleaning with Power BI

Data Cleaning with Power BI

By : Frazer
5 (7)
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Data Cleaning with Power BI

Data Cleaning with Power BI

5 (7)
By: Frazer

Overview of this book

Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization. This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You’ll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you’ll explore Power BI’s data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you’ll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier. By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.
Table of Contents (23 chapters)
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1
Part 1 – Introduction and Fundamentals
6
Part 2 – Data Import and Query Editor
11
Part 3 – Advanced Data Cleaning and Optimizations
16
Part 4 – Paginated Reports, Automations, and OpenAI

Chapter 11 – M Query Optimization

  1. B – Filtering and reducing data, using native M functions, creating custom functions, optimizing memory usage – These are the four key tips to optimizing M queries
  2. A – Parameters: table, weights, values; the weighted average is calculated by summing the weighted values and dividing by the total weight – The function takes three parameters (table, weights, values) and calculates the weighted average by summing the weighted values and dividing by the total weight.
  3. C – It loads a table into memory once, reducing memory duplication – Table.Buffer is used to load a table into memory only once, reducing memory duplication and improving query speeds on subsequent steps. Note though that it can also have the reverse effect as the initial reading and loading of the data can cause your query to run more slowly.
  4. B – Splits a table into smaller partitions for parallel processing –...

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