Book Image

Expert Data Modeling with Power BI

By : Soheil Bakhshi
Book Image

Expert Data Modeling with Power BI

By: Soheil Bakhshi

Overview of this book

This book is a comprehensive guide to understanding the ins and outs of data modeling and how to create data models using Power BI confidently. You'll learn how to connect data from multiple sources, understand data, define and manage relationships between data, and shape data models to gain deep and detailed insights about your organization. In this book, you'll explore how to use data modeling and navigation techniques to define relationships and create a data model before defining new metrics and performing custom calculations using modeling features. As you advance through the chapters, the book will demonstrate how to create full-fledged data models, enabling you to create efficient data models and simpler DAX code with new data modeling features. With the help of examples, you'll discover how you can solve business challenges by building optimal data models and changing your existing data models to meet evolving business requirements. Finally, you'll learn how to use some new and advanced modeling features to enhance your data models to carry out a wide variety of complex tasks. By the end of this Power BI book, you'll have gained the skills you need to structure data coming from multiple sources in different ways to create optimized data models that support reporting and data analytics.
Table of Contents (18 chapters)
Section 1: Data Modeling in Power BI
Section 2: Data Preparation in Query Editor
Section 3: Data Modeling
Section 4: Advanced Data Modeling

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.


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: