Overview
Great Power BI reports start long before visuals, they start with good data modelling. A well-designed model improves performance, simplifies DAX, and ensures accurate reporting.
This article briefly covers:
- Fact and dimension tables
- Star and snowflake schemas
- Relationships
- Why data modelling matters in Power BI
Fact and Dimension Tables
Fact Tables
Fact tables store measurable data such as:
- Sales amount
- Quantity
- Revenue
They usually contain many rows and grow over time.
Dimension Tables
Dimension tables store descriptive data that give context to facts:
- Customers
- Products
- Dates
Dimensions are used for filtering, grouping, and slicing data.
Example: Fact vs Dimension Tables in Power BI Model View
- Power BI Model View showing one fact table connected to multiple dimensions.
Star Schema
The star schema is the most recommended modelling approach in Power BI.
Structure
- One central fact table
- Multiple dimension tables connected directly to the fact table
Why Star Schema Works Best
- Simple and intuitive
- Fewer joins → better performance
- Easier and cleaner DAX
Star Schema Example
Power BI model with a clear star layout (fact table in the center).
Snowflake Schema
The snowflake schema is a more normalized version of the star schema.
Structure
- Dimension tables are split into multiple related tables
- Some dimensions connect indirectly to the fact table
Power BI Drawbacks
- More complex relationships
- Slower performance
- Harder to understand and maintain
For Power BI, denormalized dimensions are usually better.
Snowflake Schema Example
Model view showing dimensions branching into other dimensions.
Relationships in Power BI
Relationships control how filters flow between tables.
Best Practices
- Use one-to-many relationships
- Prefer single-direction filtering
- Avoid many-to-many unless necessary
- Ensure dimension keys are unique
Bad relationships can cause incorrect totals and confusing visuals.
Relationships View Example
Power BI relationship settings dialog or Model View relationship lines.
Why Good Data Modelling Matters
Good data modelling:
- Improves report performance
- Ensures accurate aggregations
- Simplifies DAX formulas
- Makes reports easier to scale and maintain
Bad models don’t just slow reports, they can produce misleading insights.
Final Takeaway
Data modelling is the foundation of Power BI reporting. Understanding fact and dimension tables, using a star schema, and designing clean relationships leads to faster, more reliable, and easier-to-maintain reports.
Strong data models lead to trustworthy insights.
Top comments (1)
Nice read, Judy. Your explanations are beginner-friendly and easy to understand and follow. Thank you!