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

Chapter 10: Data Modeling and Row-Level Security

In the previous chapter, we looked at the visual layer in Power BI, where a key point was to reduce the load on data sources by minimizing the complexity and number of queries. We learned that this area is usually the easiest and quickest place to apply performance-related fixes. However, experience working with a wide range of Power BI solutions has shown that issues with the underlying dataset are very common and typically have a greater negative performance impact. Importantly, this impact can be amplified because a dataset can be used by more than one report. Dataset reuse is a recommended practice to reduce data duplication and development effort.

Therefore, in this chapter, we will move one layer deeper, into modeling Power BI datasets with a focus on Import mode. Dataset design is arguably the most critical piece, being at the core of a Power BI solution and heavily influencing usability and performance. Power BI's feature...