Book Image

Microsoft Power BI Performance Best Practices

By : Bhavik Merchant
Book Image

Microsoft Power BI Performance Best Practices

By: Bhavik Merchant

Overview of this book

This book comprehensively covers every layer of Power BI, from the report canvas to data modeling, transformations, storage, and architecture. Developers and architects working with any area of Power BI will be able to put their knowledge to work with this practical guide to design and implement at every stage of the analytics solution development process. This book is not only a unique collection of best practices and tips, but also provides you with a hands-on approach to identifying and fixing common performance issues. Complete with explanations of essential concepts and practical examples, you’ll learn about common design choices that affect performance and consume more resources and how to avoid these problems. You’ll grasp the general architectural issues and settings that broadly affect most solutions. As you progress, you’ll walk through each layer of a typical Power BI solution, learning how to ensure your designs can handle scale while not sacrificing usability. You’ll focus on the data layer and then work your way up to report design. We will also cover Power BI Premium and load testing. By the end of this Power BI book, you’ll be able to confidently maintain well-performing Power BI solutions with reduced effort and know how to use freely available tools and a systematic process to monitor and diagnose performance problems.
Table of Contents (21 chapters)
1
Part 1: Architecture, Bottlenecks, and Performance Targets
5
Part 2: Performance Analysis, Improvement, and Management
10
Part 3: Fetching, Transforming, and Visualizing Data
13
Part 4: Data Models, Calculations, and Large Datasets
17
Part 5: Optimizing Premium and Embedded Capacities

Chapter 11: Improving DAX

In the previous chapter, we focused on Import datasets at the visual layer in Power BI, where a key point was to reduce the load on data sources by minimizing the complexity and number of queries that are issued to the Power BI dataset.

In theory, a well-designed data model should not experience performance issues easily unless there are extremely high data volumes with tens of millions of rows or more. However, it is still possible to get poor performance with good data models due to the way DAX measures are constructed.

Learning DAX basics is considered quite easy by many people. It can be approached by people without a technical data background but who are comfortable writing formulas in a tool such as Microsoft Excel. However, mastering DAX can be challenging. This is because DAX is a rich language with multiple ways to achieve the same result. Mastery requires having knowledge of row context and filter context, which determines what data is in...