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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

Automating refreshing of data

Data in Power BI needs to be refreshed regularly to ensure that your reports reflect the most recent information. Power Automate can be instrumental in automating the data refresh process. By setting up a workflow that triggers a data refresh at scheduled intervals or in response to specific events, you can ensure that your Power BI reports are always up to date. This is particularly useful when dealing with dynamic datasets that undergo frequent changes. Automating the data refresh not only saves time but also ensures the accuracy and relevance of your analyses.

In addition to these advantages, refreshing data works really well when you’re building complex models that leverage a number of dataflows. Instead of having to manually refresh dataflows, wait for them to complete, and then action the refresh of the semantic model; you can simply build this into a Power Automate flow. Of course, you could just schedule refreshes in Power BI, but the...

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