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

Using calculation groups

Creating calculation groups is one of the most useful features for Power BI data modelers and developers. It reduces the number of measures you have to create. Calculation groups address the fact that we have to create many measures in larger and more complex data models that are somewhat redundant. Creating those measures takes a lot of development time. For instance, in a Sales data model, we can have Sales Amount as a base measure. In real-world scenarios, we usually have to create many time intelligence measures on top of the Sales Amount measure, such as Sales Amount YTD, Sales Amount QTD, Sales Amount MTD, Sales Amount LYTD, Sales Amount LQTD, Sales Amount LMTD, and so on. We have seen models with more than 20 time intelligence measures created on top of a single measure. In real-world scenarios, we have far more base measures and a business that requires all those 20 time intelligence measures for every single base measure. You can imagine how time-consuming...