You're reading from Microsoft Power BI Data Analyst Certification Guide A comprehensive guide to becoming a confident and certified Power BI professional

Product type Paperback

Published in Jun 2022

Publisher Packt

ISBN-13 9781803238562

Length 398 pages

Edition 1st Edition

Languages

DAX

Tools

Power BI

Concepts

IT Certification

Authors (2):

Edward Corcoran

Orrin Edenfield

View More author details

Table of Contents (25) Chapters

Preface

1. Part 1 – Preparing the Data

2. Chapter 1: Overview of Power BI and the PL-300 Exam FREE CHAPTER

3. Chapter 2: Connecting to Data Sources

4. Chapter 3: Profiling the Data

5. Chapter 4: Cleansing, Transforming, and Shaping Data

6. Part 2 – Modeling the Data

7. Chapter 5: Designing a Data Model

8. Chapter 6: Using Data Model Advanced Features

9. Chapter 7: Creating Measures Using DAX

10. Chapter 8: Optimizing Model Performance

11. Part 3 – Visualizing the Data

12. Chapter 9: Creating Reports

13. Chapter 10: Creating Dashboards

14. Chapter 11: Enhancing Reports

15. Part 4 – Analyzing the Data

16. Chapter 12: Exposing Insights from Data

17. Chapter 13: Performing Advanced Analysis

18. Part 5 – Deploying and Maintaining Deliverables

19. Chapter 14: Managing Workspaces

20. Chapter 15: Managing Datasets

21. Part 6 – Practice Exams

22. Chapter 16: Practice Exams

23. Other Books You May Enjoy

Appendix: Practice Question Answers

Combining data

Data can be combined from multiple queries or even multiple source files depending on the nature of the data connected in the data model.

Power Query uses different terminology compared to technologies such as relational databases, so it's important to understand both the concepts as well as the terminology used for the PL-300 exam.

Using merge queries

Merge queries are used when we want to merge or combine columns from multiple queries where some columns are different. This often happens in data warehouse environments as data tends to be normalized or split across multiple tables (imported into a Power BI data model as a query). For more information on why data is normalized in a data warehouse, we recommend reading The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling by Ralph Kimball. Tables contain key columns that allow individual records to be merged or joined together when needed. Normalizing data is standard practice for properly...