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

Establishing a repeatable, pro-active performance improvement process

In Chapter 1, Setting Targets and Identifying Problem Areas, we learned about the potential negative impacts of poor business intelligence system performance. It is great to have knowledge, metrics, and tools to resolve performance issues. However, a behavior that I have seen all too often is that these are usually leveraged reactively after an issue has had enough of an impact on the business that it is formally raised and brought to the attention of developers and administrators. This is not a good situation to be in for reasons described in the following points:

  • Changing production systems is non-trivial, it requires careful change management, and can involve more than just deploying new technical artifacts. One example is that users may need training and documentation may need to be updated if there are significant report or dataset level changes.
  • There may be short deadlines for the business to resolve...