
Power BI Performance Optimisation
Modern Data Engineering
Fast dashboards are a sign of a well-designed data model. Our approach to high-performance Power BI solutions follows ten core commandments.
Power BI Performance Optimisation
The principle that fast dashboards are a sign of a well-designed data model is central to our BI work. Our approach to building high-performance Power BI solutions follows ten core commandments.
1. Use measures, not calculated columns
Measures calculate at query time on aggregated data; calculated columns bloat model size by storing values for every row, slowing down performance.
2. Embrace the star schema
It is the most performant structure for BI tools, avoiding the complex, multi-hop joins of normalised schemas.
3. Use incremental refresh
Configure incremental refresh for large fact tables to minimise refresh times and resource consumption.
4. Enforce query folding
Where possible, push transformations back to the source database during Power Query steps.
5. Create aggregation tables
Dramatically speed up high-level dashboards by pre-calculating summary-level data.
6. Implement RLS via roles
Use the built-in Row-Level Security engine, which is more secure and performant than applying complex DAX filters in each measure.
7. Limit visuals per page
Each visual fires at least one query. We aim for fewer than 8 visuals to ensure a responsive user experience.
8. Default to Import mode
Use DirectQuery sparingly and only when absolutely necessary for near real-time data needs, as it is significantly less performant.
9. Use variables in complex DAX
Store intermediate calculations in variables to improve readability, debugging, and query performance.
10. Profile performance with DAX Studio
We don't guess where bottlenecks are; we use external tools to analyse query plans and identify the root cause of slow performance.
Put our expertise to work
These frameworks are the starting point for our client engagements. If you're facing similar challenges, the next step is a complimentary discovery call.
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