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
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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
Takeaways from power bi schema and snowflake:
• 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
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