If you have shared your Power BI dataset with others in your organization for self-service analytics, you may be interested in understanding what queries they are using, as well as the performance and impact of these queries on the dataset and overall system capacity and health.

Even more interesting, with the concepts of Microsoft Fabric, where data is loaded on demand, it is more important to know which data you want to keep in active memory and keep warm, rather than what gets evicted from the system.

During this session, we will explore Azure Log Analytics integration on Power BI datasets to measure engine performance, query logging and other telemetry to gain deeper insights for further optimization of your Power BI datasets.

After this session you will be able to:
- Understand how to measure query performance
- Analyze which columns and tables are being used
- Know how to configure Log Analytics on your datasets
- Be able to setup a system to keep your data for direct lake datasets warm and in memory.

During this session you will find out how to measure query performance for Power BI datasets. As a next step we will delve into Direct Lake storage mode for Fabric to understand how data gets evicted from the system and how you can keep the data warm for optimal performance. In order to do this, we will explore Log Analytics integration on Power BI.