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Expert Data Modeling with Power BI

You're reading from   Expert Data Modeling with Power BI Get the best out of Power BI by building optimized data models for reporting and business needs

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Product type Paperback
Published in Jun 2021
Publisher Packt
ISBN-13 9781800205697
Length 612 pages
Edition 1st 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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Table of Contents (18) Chapters Close

Preface 1. Section 1: Data Modeling in Power BI
2. Chapter 1: Introduction to Data Modeling in Power BI FREE CHAPTER 3. Chapter 2: Data Analysis eXpressions and Data Modeling 4. Section 2: Data Preparation in Query Editor
5. Chapter 3: Data Preparation in Power Query Editor 6. Chapter 4: Getting Data from Various Sources 7. Chapter 5: Common Data Preparation Steps 8. Chapter 6: Star Schema Preparation in Power Query Editor 9. Chapter 7: Data Preparation Common Best Practices 10. Section 3: Data Modeling
11. Chapter 8: Data Modeling Components 12. Chapter 9: Star Schema and Data Modeling Common Best Practices 13. Section 4: Advanced Data Modeling
14. Chapter 10: Advanced Data Modeling Techniques 15. Chapter 11: Row-Level Security 16. Chapter 12: Extra Options and Features Available for Data Modeling 17. Other Books You May Enjoy

Creating Dimensions tables

We should already be connected to the Chapter 6, Sales Data.xlsx file from Power Query Editor. We need to analyze each dimension from a business perspective and create dimensions, if they need to be created.

Geography

Looking at the identified business requirements shows that we have to have a dimension that keeps geographical data. When we look at the data, we can see that there are geography-related columns in the Sales table. We can create a separate dimension for Geography that's derived from the Sales table. However, this might not cover all business requirements.

Let's have another look at the Potential Dimensions table, shown in the following figure, which shows some geography-related columns in the Customer table. We need to find commonalities in the data to combine the data from both tables into a single Geography dimension. Using Column Distribution shows that the CustomerKey column is a primary key of the Customer table:

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