Star Schema vs. Snowflake Schema in Power BI

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1?? Star Schema ??

? Structure:

• A central fact table is directly connected to dimension tables.
• Dimension tables are denormalized, meaning they contain redundant data for better performance.

? Advantages:

? Faster query performance (fewer joins).

? Simpler model, easier to understand and maintain.

? Best suited for Power BI's VertiPaq engine (optimized for columnar storage).

? Example: Imagine a sales dataset:

• Fact Table: Sales (Date, Product ID, Customer ID, Sales Amount)
• Dimension Tables: Date, Product, Customer, Region

?? All dimensions are directly connected to the fact table.

2?? Snowflake Schema ?

? Structure:

• Fact table is connected to normalized dimension tables.
• Dimension tables are further split into sub-dimensions, reducing redundancy.

? Advantages:

? Less storage needed (reduces data duplication).

? Maintains data integrity by avoiding redundancy.

? Better suited for OLAP systems where normalization is preferred.

? Example: Expanding the Star Schema above:

• Fact Table: Sales (Date, Product ID, Customer ID, Sales Amount)
• Dimension Tables:Product → Product CategoryCustomer → Customer RegionDate → Fiscal Calendar

?? Dimensions are broken down into smaller tables, requiring more joins.

?? Key Differences

?? Which One to Use in Power BI?

?? Star Schema is preferred in Power BI because DAX and Power BI’s VertiPaq engine are optimized for denormalized data structures.

?? Snowflake Schema is useful if data integrity and storage efficiency are priorities, but it may lead to performance issues due to excessive joins.

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