Chapter 6. Optimizing Enterprise-Scale Semantic Models
As your semantic model increases in size, its performance may become an issue. For smaller models, workarounds might work, but as your models grow, you’ll discover that optimization is not only nice to have—it’s crucial. A sluggish model can impact all the things you produce, including your Power BI reports, which must have quick-loading visuals and efficient refreshes so that end users will view them as workable. Over time, these performance challenges can create significant headaches for both the people working with the data and the end users.
This chapter will focus on helping you optimize performance by making smarter decisions on how to query, store, and refresh data. We’ll begin by identifying performance issues with DAX INFO.VIEW functions and the Power BI performance analyzer. Next, we’ll look at practical techniques for improving DAX performance, such as refining your measures. Then, we’ll look into Direct Lake, which is a storage mode that’s especially suitable for large semantic models, including those in enterprise-scale operations. Lastly, we’ll cover incremental refresh, which allows you to update only newly added data rather than reloading all data repeatedly. This becomes essential when you’re dealing with large semantic models with large amounts of frequently added data.
Understanding Performance Metrics in DAX Queries and Report Visuals
Slow performance can frustrate users and hurt adoption when building ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access