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 how to speed up Power BI datasets in Import mode. We began with some theory on Kimball dimensional modeling, where we learned about star schemas, which are built from facts and dimensions. Data modeling is about grouping and relating attributes, and star schemas are one way to model data. They provide non-technical users with an intuitive way to analyze data by combining qualitative attributes into dimension tables. These dimensions are related to fact tables, which contain qualitative attributes. Power BI's Analysis Services engine works extremely well with star schemas, which are preferred. Hence, we briefly looked at the four-step dimensional modeling process and provided a practical example, including one with many-to-many relationships.

Then, we focused on reducing the size of datasets. This is important because less data means less processing, which results in better performance and more free resources for other parallel operations...