The Model Matters More Than the Visuals
A slow Power BI report isn't fixed with better DAX or fewer visuals. It's fixed in the data model—before you build anything users can see.
Power BI was built for analytical processing (OLAP), not transactional systems (OLTP). That matters. The normalized database designs that work for operational systems often choke in Power BI. The way you structure tables and relationships determines query speed, calculation accuracy, and whether your model scales or collapses under load.
Worth noting: good modeling lets you scale to millions or billions of rows with strong performance. Bad modeling hits the wall much earlier.
Facts, Dimensions, and Why the Distinction Matters
Fact tables hold measurable business events: sales transactions, web analytics, manufacturing outputs, financial records. High row counts, numeric values you'll aggregate, foreign keys pointing to dimensions.
Dimension tables provide context: product catalogs, customer directories, date calendars, employee rosters. Low row counts, descriptive attributes you'll filter and group by.
The mental shortcut: if you're summing it, it's a fact. If you're filtering by it, it's a dimension.
Star Schemas: Power BI's Preferred Architecture
The star schema—fact table at center, dimensions radiating outward—is the recommended approach for enterprise Power BI models. Not because it looks tidy in Model view. Because it performs.
What makes it work:
- Direct relationships between facts and dimensions
- No dimension-to-dimension links
- Normalized structure that matches how Power BI's engine queries data
- Single-direction relationships (avoid bi-directional and many-to-many—they introduce filtering ambiguity and performance hits)
The snowflake schema trap: Some teams normalize dimensions into sub-dimensions (snowflake schema). Looks logical. Kills query speed. Power BI's engine wasn't optimized for this. Star schemas win on performance.
The Practices That Scale
Enterprise teams report these as essential:
- Integer keys for relationships, not text
- Hide fact table foreign keys from report view
- Minimize calculated columns—they bloat model size and slow refresh
- Use web-based data sources over local files
- Partition large tables for incremental refresh
- Build proper date tables (don't rely on auto-generated ones)
Skepticism worth noting: many-to-many relationships are sometimes necessary for complex scenarios, but they're often a sign you need to rethink your model. They add filtering complexity and degrade performance. Single-direction is preferred.
The Real Trade-Off
You can push optimization upstream—clean data in Databricks or your warehouse—or fix it in Power BI. The upstream approach scales better. But many teams inherit messy data sources and need to make Power BI work anyway.
The model is where that battle is won or lost. Get the architecture right, and DAX becomes simpler, queries run faster, and reports scale with your business. Get it wrong, and you're fighting performance problems you can't DAX your way out of.
The fine print: No amount of visual optimization fixes a badly designed model. But a well-designed model makes everything else easier.