Book Image

Data Cleaning with Power BI

By : Gus Frazer
Book Image

Data Cleaning with Power BI

By: Gus 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)
Free Chapter
1
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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

aggregated tables

cons 207

considerations 207

pros 207

AI

challenges, tackling with 294, 295

AI insights

examples 146

analytics (BI) managers 21

anomaly detection

examples 134

automated machine learning (AutoML) 134

implementing 135-145

use cases 134

automation

triggers, handling 260, 261

B

bidirectional cross-filtering (BDCF) 125, 210, 222

applying, results to model 213

best practices 213, 214

creating 210-212

using 209

big data 218

handling, best practices 218-220

working, challenges in Power BI 218

business intelligence (BI) 6, 21

business users 21

C

calculated columns 41

considerations 44

versus measures 41

Calculation group 43

calendars

adding, reasons 204, 205

cardinality 214, 215

...