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 aggregations

From a data analytics viewpoint, the concept of aggregation tables has been around for a long time. The concept was widely used in SQL Server Analysis Service Multi-Dimensional. The way aggregation tables work is simple: we aggregate the data at a particular grain and make it available in the data model. Already aggregating the data that's available in the model usually translates into better performance at runtime. However, the aggregation typically happens at a different level of granularity. Therefore, we change the granularity of the base table. Now, you may be wondering, so what if the business needs to drill down to a lower granular level of data? The answer is that we need to keep the base table available in the data model. We aggregate the base table at a different granular level in a new table. Then, we implement a control mechanism to detect the level of granularity that the user is at. If the data is available at the aggregated level, the calculation...