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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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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

Planning for your custom function

Creating custom functions in Power Query can be a powerful tool for automating and streamlining your data transformation processes. However, before you dive into the world of custom functions, it’s essential to plan your approach carefully. In this section, we’ll explore the key aspects of planning for your custom function, including defining the problem, identifying parameters, and setting clear objectives. Proper planning will ensure that your custom functions are efficient, effective, and aligned with your data preparation needs.

Defining the problem

The first step in planning for a custom function is to clearly define the problem you want to solve. What specific data transformation or manipulation task do you need to perform regularly?

For example, you might need to calculate the rolling average of sales data or create a custom date hierarchy. Identifying the problem is essential as it serves as the foundation for building...

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