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)
Part 1: Architecture, Bottlenecks, and Performance Targets
Part 2: Performance Analysis, Improvement, and Management
Part 3: Fetching, Transforming, and Visualizing Data
Part 4: Data Models, Calculations, and Large Datasets
Part 5: Optimizing Premium and Embedded Capacities


In this chapter, we began to dive deeper into specific areas of an actual Power BI solution, starting from transforming and loading data. We saw how Power Query and the mashup engine take center stage in this part of the pipeline, powered by the M query language. We learned how memory and CPU are important for data refresh operations. This meant that poor Power Query design can lead to failed or long-running data refreshes due to resource exhaustion.

Additionally, we learned about parallelism and how you can change the settings in Power BI Desktop to improve performance. There are also settings that can be adjusted in Power BI Desktop to speed up the developer experience and optimize data loading in general. We also learned how to customize refresh parallelism in Power BI Premium, Embedded, and Azure Analysis Services.

Then, we moved on to transformations, focusing on typical operations that can slow down with large volumes of data such as filtering, joining, and aggregating...