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

Introduction to Power Query features for data modelers

This section looks at some features currently available within Power Query Editor that help data modelers identify and fix errors quicker. Data modelers can get a sense of data quality, statistics, and data distribution within a column (not the overall dataset). For instance, a data modeler can quickly see a column's cardinality, how many empty values a column has, and so on and so forth.


As previously mentioned, the information provided by the Column quality, Column distribution, and Column profile features is calculated based on the top 1000 rows of data (by default), which in some cases leads to false information. It is good practice to set Column profile to get calculated based on the entire dataset for smaller amounts of data. However, this approach may take a while to load the column profiling information for larger amounts of data, so be careful while changing this setting if you are dealing with large tables...