Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Data Cleaning with Power BI
  • Table Of Contents Toc
  • Feedback & Rating feedback
Data Cleaning with Power BI

Data Cleaning with Power BI

By : Frazer
5 (7)
close
close
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)
close
close
Free Chapter
1
Part 1 – Introduction and Fundamentals
In Progress | 0 / 1 sections completed | 0%
6
Part 2 – Data Import and Query Editor
In Progress | 0 / 1 sections completed | 0%
11
Part 3 – Advanced Data Cleaning and Optimizations
In Progress | 0 / 1 sections completed | 0%
13
Chapter 10: Creating Custom Functions in Power Query
In Progress | 0 / 6 sections completed | 0%
16
Part 4 – Paginated Reports, Automations, and OpenAI
In Progress | 0 / 1 sections completed | 0%
21
Index
In Progress | 0 / 2 sections completed | 0%

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...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech

Create a Note

Modal Close icon
You need to login to use this feature.
notes
bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Delete Note

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY