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

General data transformation guidance

Power Query allows users to build relatively complex data transformation pipelines through a point and click interface. Each step of the query is defined by a line of M script that has been autogenerated by the UI. It's quite easy to load data from multiple sources and perform a wide range of transformations in a somewhat arbitrary order. Suboptimal step ordering and configuration can use unnecessary resources and slow down the data refresh. Sometimes, the problem might not be apparent in Power BI Desktop. This is more likely when using smaller subsets of data for development, which is a common practice. Hence, it's important to apply good Power Query design practices to avoid surprises. Let's begin by looking at how Power Query uses resources.

Data refresh, parallelism, and resource usage

When you perform a data refresh for an Import mode dataset in the Power BI service, the dataset stays online. It can still be queried by...