You have heard that a star schemas are optimal for Power BI. You have seen the dramatic effect of compression on your data models. And the storage engine makes parallelism happen? Or something like that!

Have you ever wondered *why* these things are true and how they work? Or how you can use this knowledge to make models that perform better?

This session consists of a deep dive into the drivers of performance in analytical databases, and the specific optimizations of the Vertipaq storage engine, compression, and query execution. Then, we will build upon this understanding to optimize models to work well with VertiPaq. You will learn *why* certain patterns of models are optimizations, not just what those patterns are.

This includes approximately 1/3 introduction and theory and extended worked examples and model optimizations both for total size in RAM and query performance at report time.

Coming out of this session, you will understand VertiPaq at a deeper level and be able to identify memory and performance optimizations for your models.

Attendees should be comfortable with the basics of DAX and have built multiple datasets in Power BI or Analysis Services.