When working with Power BI, most beginners focus heavily on visuals—charts, tables, slicers, and dashboards. However, the real foundation of reliable, fast, and accurate Power BI reports is data modelling.
A poorly designed model can lead to:
Slow reports
Incorrect totals
Confusing relationships
Hard-to-maintain dashboards
In this article, we’ll explore schemas and data modelling in Power BI, focusing on:
Star schema
Snowflake schema
Fact and dimension tables
Relationships
Why good modelling is critical for performance and accurate reporting
What Is Data Modelling in Power BI?
Data modelling is the process of structuring your data into tables and defining how those tables relate to each other.
In Power BI, this happens in the Model view, where you:
Organize tables
Create relationships
Decide filter directions
Design a structure that supports efficient analysis
Think of data modelling as designing the blueprint before building the house.
Fact Tables vs Dimension Tables
Before discussing schemas, it’s important to understand the two main table types.
Fact Tables
Fact tables store measurable, numerical data.
Examples:
Sales amount
Quantity sold
Revenue
Cost
Profit
Characteristics:
Usually very large
Contain foreign keys to dimensions
Contain metrics used in calculations
Example:
Fact_Sales
DateKey ProductKey CustomerKey SalesAmount Quantity
Dimension Tables
Dimension tables store descriptive attributes used for filtering and grouping.
Examples:
Product name
Customer name
Region
Category
-Date details
Characteristics:
Smaller than fact tables
Contain descriptive columns
Used in slicers and axes
Example:
Dim_Product
ProductKey ProductName Category Brand
What Is a Star Schema?
The star schema is the recommended and most efficient data model for Power BI.
Structure
One central fact table
Multiple dimension tables
Dimensions connect directly to the fact table
The model visually resembles a star
Example:
Dim_Date Dim_Product Dim_Customer
\ | /
Fact_Sales
Why Star Schema Is Best for Power BI
- Simple relationships
- Faster performance
- Easier DAX calculations
- Clear filter flow
- Easier to understand and maintain
Power BI’s engine VertiPaq is optimized for star schemas.
Example Star Schema in Power BI
Fact_Sales
Dim_Date
Dim_Product
Dim_Customer
Dim_Region
Each dimension connects one-to-many to the fact table.
What Is a Snowflake Schema?
A snowflake schema is a variation of the star schema where dimension tables are further normalized into sub-dimensions.
Structure
Fact table at the center
Dimension tables split into multiple related tables
More relationships
Example:
Dim_Product → Dim_Category
\
Fact_Sales
When Snowflake Schema Appears
Data comes directly from normalized databases
Dimensions have many hierarchical levels
Storage optimization is a priority
Drawbacks in Power BI
- More complex relationships
- Slower performance
- Harder DAX calculations
- Confusing filter paths
In Power BI, denormalizing dimensions back into a star schema is usually recommended.
Relationships in Power BI
Relationships define how tables filter each other.
Common Relationship Type
One-to-Many (1:*)
Dimension (1)
Fact (*)
Example:
Dim_Product[ProductKey] → Fact_Sales[ProductKey]
**Relationship Direction##
Power BI relationships usually use:
Single direction (Dimension → Fact)
Avoid:
Bi-directional filters unless absolutely necessary
Many-to-many relationships (performance risk)
Why Good Data Modelling Is Critical
##Performance##
Star schema reduces joins
Smaller, denormalized dimensions compress better
Faster report loading and interactions
Accurate Calculations
Bad models cause:
Double counting
Incorrect totals
Broken time intelligence
Good models ensure:
Correct aggregation
Predictable DAX behavior
##Simpler DAX##
With a clean star schema:
Measures are shorter
Logic is clearer
Debugging is easier
Example:
Total Sales = SUM(Fact_Sales[SalesAmount])
No complex filters needed.
##Easier Maintenance##
Adding new visuals is straightforward
New measures don’t break existing reports
New data sources integrate cleanly
Best Practices for Power BI Data Modelling
- Use star schema whenever possible
- Separate facts and dimensions
- Avoid unnecessary bi-directional filters
- Use surrogate keys (IDs)
- Flatten snowflake dimensions when possible
- Validate relationships early
- Keep the model simple and readable
Conclusion
In Power BI, great visuals come from great models.
You can have the best charts in the world, but without:
Proper schemas
Clean relationships
Well-defined fact and dimension tables
your reports will be slow, inaccurate, and difficult to trust.
Mastering data modelling is what separates a Power BI user from a Power BI professional.
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