SQLBits 2023
Lessons learned from optimizing enterprise data models
What should you do when DirectQuery is the ONLY available option? How to reduce the data model size, while at the same time satisfying business requirements?
I'll share tips, tricks and workarounds learned and implemented in real-life scenarios on enterprise data models.
You know that DirectQuery performance sucks - but, what should you do when DirectQuery is the ONLY available option?
You know that high cardinality increases data model size - but, what should you do when your users want to analyze numbers on a granularity level higher than day? How to reduce the data model size, while at the same time satisfying business requirements?
You know that by leveraging composite models, you can gain significant performance improvements - but why do you still have DirectQuery queries even when you target the data from the aggregated table in Import mode?
In this session, I'll share tips, tricks and workarounds learned and implemented in real-life scenarios on enterprise data models.
As you may assume, this session is not for rookies - I'll assume that you already have at least intermediate Power BI/Tabular model knowledge.
You know that high cardinality increases data model size - but, what should you do when your users want to analyze numbers on a granularity level higher than day? How to reduce the data model size, while at the same time satisfying business requirements?
You know that by leveraging composite models, you can gain significant performance improvements - but why do you still have DirectQuery queries even when you target the data from the aggregated table in Import mode?
In this session, I'll share tips, tricks and workarounds learned and implemented in real-life scenarios on enterprise data models.
As you may assume, this session is not for rookies - I'll assume that you already have at least intermediate Power BI/Tabular model knowledge.