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Expert Data Modeling with Power BI - Second Edition

You're reading from  Expert Data Modeling with Power BI - Second Edition

Product type Book
Published in Apr 2023
Publisher Packt
ISBN-13 9781803246246
Pages 698 pages
Edition 2nd Edition
Languages
Author (1):
Soheil Bakhshi Soheil Bakhshi
Profile icon Soheil Bakhshi

Table of Contents (22) Chapters

Preface Section I: Data Modeling in Power BI
Introduction to Data Modeling in Power BI Data Analysis eXpressions and Data Modeling Section II: Data Preparation in Query Editor
Data Preparation in Power Query Editor Getting Data from Various Sources Common Data Preparation Steps Star Schema Preparation in Power Query Editor Data Preparation Common Best Practices Section III: Data Modeling
Data Modeling Components Star Schema and Data Modeling Common Best Practices Section IV: Advanced Data Modeling
Advanced Data Modeling Techniques Row-Level and Object-Level Security Dealing with More Advanced Data Warehousing Concepts in Power BI Introduction to Dataflows DirectQuery Connections to Power BI Datasets and Analysis Services in Composite Models New Options, Features, and DAX Functions Other Books You May Enjoy
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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