Building good-looking dashboards in Power BI is great—but if the data model behind them isn’t solid, things can fall apart fast. As your reports get more complex and more people rely on them, having the right modelling setup makes a huge difference. It’s not just about speed—it’s about getting better insights too.
In this post, I’ll walk you through some advanced data modelling tips in Power BI that can help you take your reports to the next level. I'll include 10 important points to make your report blazing fast and easy on the capacities.
1. Star Schema Design for Performance and Simplicity
One of the core principles of efficient data modelling in Power BI is using a star schema instead of a flat or snowflake schema. This approach separates fact tables (quantitative data) and dimension tables (descriptive attributes), making it easier to manage relationships and improve query performance.- Benefits:
- Simplifies DAX calculations.
- Reduces ambiguity in relationships.
- Enhances performance due to reduced cardinality.
2. Managing Relationships and Cardinality
Power BI allows relationships between tables to be set as single or both directional. Using bidirectional filters incorrectly can cause performance issues or ambiguous query results.- Best Practices:
- Use single-direction relationships wherever possible.
- Avoid many-to-many relationships unless necessary.
- Regularly review cardinality, high-cardinality columns (like GUIDs or transaction IDs) can slow down your model.
3. Using DAX Measures Over Calculated Columns
While both DAX measures and calculated columns are useful, measures are generally more efficient because they are calculated on demand, while calculated columns are stored in memory.- Tips:
- Prefer measures for aggregations and calculations.
- Use calculated columns only when filtering or slicing data by a new column is absolutely necessary.
4. Incremental Refresh for Large Datasets
If you're working with large datasets, incremental refresh is a game-changer. Instead of refreshing the entire dataset daily, Power BI can refresh only new or modified data.- Steps:
- Define parameters for data ranges.
- Set up incremental refresh policies in Power BI Desktop.
- Publish to Power BI Service for scheduled incremental updates.
5. Leveraging Aggregations for Faster Queries
- How to use:
- Create summary tables using Power Query or DAX.
- Set up aggregations in the model and define mappings.
- Use the “Manage Aggregations” feature to configure behaviour.
6. Optimize Column Data Types and Reduce Model Size
Minimizing model size can lead to major performance improvements. Simple practices like:- Removing unused columns and tables.
- Using correct data types (e.g., using integers instead of strings).
- Avoiding unnecessary precision in decimals can make a big difference.
7. Make the Most of Query Folding
Query folding is one of those behind-the-scenes features in Power BI that can really boost performance, especially when you're pulling in large datasets. It basically means Power BI is smart enough to push transformations (like filtering or grouping) back to the source system, instead of doing all the heavy lifting on your local machine.
- Why it matters:
- If folding happens, your data source does the work. If it doesn’t, Power BI has to process it, which can slow things down especially during refreshes.
- Tip:
- Try to keep your Power Query steps as "foldable" as possible. Stick to transformations early in the process that Power BI can push back to the source (like SQL Server). Once you do something that breaks folding like merging queries or adding custom, columns. it’s often game over for folding in that step chain.
8. Use Composite Models for Flexibility
Composite models allow you to combine Import and DirectQuery storage modes in a single model. This is great when you need real-time data access for certain tables but want high performance for historical data.- Use cases:
- Real-time operational dashboards.
- Blending current and historical data.
9. Understand How Query Caching Works
- Why it matters:
- This makes your reports feel snappier and reduces the load on your data model. But caching only works well when your model is optimized—so things like clean relationships, efficient DAX, and minimal unnecessary visuals still play a big role.
- Tip:
- Keep your reports clean and focused. Too many visuals or overly complex queries can prevent caching from kicking in effectively. Also, remember: caching helps performance for viewing reports, but it’s not a substitute for good modelling and refresh strategies.
10. Be Smart About Calculated Tables
Calculated tables can be super handy in Power BI when you need to create reference data or summaries that don’t exist in your source systems. But like calculated columns, they live in memory and can bloat your model if you’re not careful.- Why it matters:
- Every calculated table adds to your model size and refresh time. They’re recalculated every time the data refreshes, which can be overkill if the logic can be handled in Power Query or at the source.
- Tip:
- Use calculated tables sparingly. Ask yourself: Can I build this logic in Power Query instead? If yes, do it there. Calculated tables are best reserved for things like dynamic calendar tables or bridging tables that truly depend on DAX logic.
