Book Image

Mastering Microsoft Power BI – Second Edition - Second Edition

By : Gregory Deckler, Brett Powell
4.5 (2)
Book Image

Mastering Microsoft Power BI – Second Edition - Second Edition

4.5 (2)
By: Gregory Deckler, Brett Powell

Overview of this book

Mastering Microsoft Power BI, Second Edition, provides an advanced understanding of Power BI to get the most out of your data and maximize business intelligence. This updated edition walks through each essential phase and component of Power BI, and explores the latest, most impactful Power BI features. Using best practices and working code examples, you will connect to data sources, shape and enhance source data, and develop analytical data models. You will also learn how to apply custom visuals, implement new DAX commands and paginated SSRS-style reports, manage application workspaces and metadata, and understand how content can be staged and securely distributed via Power BI apps. Furthermore, you will explore top report and interactive dashboard design practices using features such as bookmarks and the Power KPI visual, alongside the latest capabilities of Power BI mobile applications and self-service BI techniques. Additionally, important management and administration topics are covered, including application lifecycle management via Power BI pipelines, the on-premises data gateway, and Power BI Premium capacity. By the end of this Power BI book, you will be confident in creating sustainable and impactful charts, tables, reports, and dashboards with any kind of data using Microsoft Power BI.
Table of Contents (18 chapters)
16
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17
Index

Performance testing

Given that DAX measures can implement complex business logic and are dynamically calculated as users interact with reports, the performance of these calculations is a critical component of providing a good user experience.

There are often many available methods of implementing business logic and custom filter contexts into DAX measures. Although these alternatives deliver the essential functional requirements, they can have very different performance characteristics, which can ultimately impact user experience and the scalability of a dataset.

When migrating a self-service dataset to a corporate solution or preparing a large and highly utilized dataset, it’s always a good practice to test common queries and the DAX measures used by those queries.

For example, the same common dimension grouping (for example, Product Category and Year) and the same filter context (Year = 2018) could produce dramatically different performance results based on the...