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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

Optimizing memory usage

Managing memory usage is vital for query optimization. Let’s consider an example where you’re dealing with a large dataset with repeated data. Instead of creating multiple copies of the same data, we can explore the Table.Buffer function.

This function loads a table into memory once, reducing memory duplication, which can lead to improved query speed, especially for large datasets with repeated data. This optimization can result in more efficient use of system resources and better overall performance during data transformation and analysis tasks.

On the other hand, though, there are some potential drawbacks to be aware of. One significant downside is that using Table.Buffer can actually slow down performance in certain scenarios.

One reason for this is that it loads the entire table into memory at once. For very large datasets, this can consume a significant amount of memory resources, potentially leading to memory pressure and slower...

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