This content originally appeared on DEV Community and was authored by Kenyansa Felix Amenya
Power BI: Star Schema vs Snowflake Schema
Star Schema
A star schema is defined as the simplest data warehouse schema where one or more fact tables reference any number of dimension tables in a star-like structure.
Structure
• Fact Table: Central table containing business metrics and foreign keys
• Dimension Tables: Surrounding tables connected directly to the fact table
• Denormalized: Dimension tables contain all related data
Example of star schema
Advantages
• Simpler Queries: Fewer JOINs required
• Better Performance: Faster query execution
• Easy to Understand: Intuitive structure for business users
• Optimized for Reporting: Ideal for Power BI and analytics
• Reduced Complexity: Minimal table relationships
Disadvantages
• Data Redundancy: Repeated data in dimension tables
• Storage Inefficiency: Larger storage requirements
• Update Anomalies: Potential data inconsistency
• Less Flexible: Harder to accommodate changes
Snowflake Schema
A snowflake schema is defined as a normalized version of the star schema where dimension tables are broken down into multiple related tables.
Structure
• Fact Table: Central table with foreign keys
• Normalized Dimensions: Hierarchical dimension tables
• Multiple Levels: Dimensions split into sub-dimensions
Example of a snowflake schema
Advantages
• Reduced Data Redundancy: Normalized structure
• Storage Efficiency: Smaller storage footprint
• Data Integrity: Better consistency
• Flexibility: Easier to accommodate changes
• Better for OLTP: Closer to operational databases
Disadvantages
• Complex Queries: More JOINs required
• Slower Performance: Reduced query speed
• Harder to Understand: More complex for business users
• Maintenance Overhead: More tables to manage
When to Use Each Schema
Use Star Schema When:
• Primary Use Case: Business intelligence and reporting
• Performance Critical: Fast query response needed
• Business User Focus: End users need simplicity
• Power BI/Tableau: Optimized for visualization tools
• Read-Intensive: Heavy reporting workload
• Data Marts: Department-specific analytics
Use Snowflake Schema When:
• Primary Use Case: Complex data relationships
• Storage Constraints: Limited storage capacity
• Data Integrity: High consistency requirements
• Source System: Mirroring normalized source data
• ETL Processes: Easier incremental loading
• Regulatory Compliance: Strict data governance
Power BI Considerations
Star Schema is Recommended Because:
- DAX Optimization: Better performance with measures
- Relationship Simplicity: Cleaner model relationships
- User-Friendly: Easier for report consumers
- Query Performance: Faster refresh and calculation
- Best Practice: Microsoft's recommended approach
Reference Table showing the differences
Aspect Star Schema Snowflake Schema
Performance High Low
Storage Low High
Complexity High low
Flexibility Minimal Highly flexible
Data Integrity Low data integrity High data integrity
Ease of Use Very simple to use complex
Conclusion
For Power BI implementations, the star schema is generally preferred due to its performance benefits, simplicity, and alignment with business reporting needs. However, understanding both schemas allows you to make informed decisions based on specific project requirements, data complexity, and organizational constraints.
Recommendation: Always Start with star schema and only snowflake when specific normalization benefits outweigh the performance costs.
Key Takeaways:
• Star Schema = Performance + Simplicity
• Snowflake Schema = Storage Efficiency + Data Integrity
• Power BI prefers Star Schema for better performance
• Choose based on your specific use case and constraints
This content originally appeared on DEV Community and was authored by Kenyansa Felix Amenya
Kenyansa Felix Amenya | Sciencx (2025-11-17T11:54:01+00:00) Power BI: Star Schema vs Snowflake Schema. Retrieved from https://www.scien.cx/2025/11/17/power-bi-star-schema-vs-snowflake-schema/
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