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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

Understanding DAX

DAX is a formula and query language that plays a pivotal role in Power BI, helping users of Power BI to perform complex calculations and analysis on their data. It’s a language created by Microsoft for their suite of products and was first introduced in 2009 along with Power Pivot for Excel, something that was then also incorporated into Power BI. Helping to create and define custom calculations and formulas goes beyond the capabilities of traditional Excel functions.

Interestingly, it originated from the need to bridge the gap between relational database systems and traditional spreadsheet tools to help lower the barrier for professionals by providing a formula language that was more user-friendly for business analysts who may not be SQL experts, hence why DAX has been designed to work with tabular data models. Microsoft recognized the limitations of Excel at handling large sets of data and complex calculations, and this then led them to develop DAX, which...

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