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

Summary

In this chapter, we learned that DAX tuning is important because inefficient formulas can impact performance, even with well-designed datasets. This is because the DAX pattern directly influences how Analysis Services retrieves data and calculates query results.

We looked at a process for DAX tuning using tools that were introduced earlier in this book. First, we suggested using the Best Practice Analyzer and manual reviews to identify DAX improvements and then prioritize the changes to handle trivial fixes. Then, we suggested using Desktop Performance Analyzer to capture the queries that have been generated by visuals and running them in DAX Studio to understand their behavior. It is important to look at the total duration, number of internal queries, and time spent in the formula engine versus the storage engine. Once the changes have been prototyped and verified in DAX Studio, they can be made in the dataset; reports should be checked in production scenarios for performance...