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 introduced a repeatable process to help you manage performance pro-actively in your organization. This is important for consistency and the overall satisfaction of users. If we can catch and repair issues before they become widespread, we can save a lot of time and money.

We looked at how to establish baselines as a starting point and how it's important to have the correct granularity of model, report page, timeframe, user permissions, and other factors for the baselines. We talked about maintaining performance history so that you can establish meaningful trends and spot seasonal issues. When problematic content is identified, we recommended that the remediation work is prioritized based on business value and user impact to maximize return on investment. That investment involved metrics and tools we described in previous chapters that help profile the system and highlight slow areas. We then learned that taking learnings from any fixes back into...