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Expert Data Modeling with Power BI, Second Edition

You're reading from   Expert Data Modeling with Power BI, Second Edition Enrich and optimize your data models to get the best out of Power BI for reporting and business needs

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Product type Paperback
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246246
Length 698 pages
Edition 2nd Edition
Languages
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Author (1):
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Soheil Bakhshi Soheil Bakhshi
Author Profile Icon Soheil Bakhshi
Soheil Bakhshi
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Toc

Table of Contents (22) Chapters Close

Preface 1. Section I: Data Modeling in Power BI
2. Introduction to Data Modeling in Power BI FREE CHAPTER 3. Data Analysis eXpressions and Data Modeling 4. Section II: Data Preparation in Query Editor
5. Data Preparation in Power Query Editor 6. Getting Data from Various Sources 7. Common Data Preparation Steps 8. Star Schema Preparation in Power Query Editor 9. Data Preparation Common Best Practices 10. Section III: Data Modeling
11. Data Modeling Components 12. Star Schema and Data Modeling Common Best Practices 13. Section IV: Advanced Data Modeling
14. Advanced Data Modeling Techniques 15. Row-Level and Object-Level Security 16. Dealing with More Advanced Data Warehousing Concepts in Power BI 17. Introduction to Dataflows 18. DirectQuery Connections to Power BI Datasets and Analysis Services in Composite Models 19. New Options, Features, and DAX Functions 20. Other Books You May Enjoy
21. Index

Creating Dimension tables

We should already be connected to the Chapter 6, Sales Data.xlsx file from Power Query Editor. In this section, we look at the necessity of creating the potential dimensions identified in the previous section. We first evaluate each dimension from a business requirement perspective. If we are convinced that the dimension is required, we create it.

Geography

The identified business requirements show that we must have a dimension keeping geographical data. When we look at the data, we can see geography-related columns in the Sales table. We can create a separate Geography dimension derived from the Sales table. However, this might not cover all business requirements.

As the following image shows, there are some geography-related columns in the Customer table. We must find commonalities in the data and think about the possibility of combining the data from both tables into a single Geography dimension. Using Column Distribution shows that the CustomerKey...

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