How each design works, a side-by-side comparison, a simple SQL example, when to use which, and how data engineers build them in Azure and Power BI. This Snowflake Schema vs Star Schema guide keeps it simple.
✍️ Azure Trainings trainers⏱ 4 min read📅 Updated 11 October 2026
Both designs have a central fact table (numbers you measure, like sales) surrounded by dimension tables (descriptions, like product, customer and date). A star schema keeps each dimension in one table — simpler and faster for reporting. A snowflake schema splits dimensions into several related tables — less duplication, but more joins. For Power BI and most BI, use a star schema.
Describes the facts — product, customer, store or date details.
Example: DimProduct, DimDate
Design 1 · Star
What is a star schema?
A fact table in the centre connected directly to denormalised dimension tables. Drawn on paper, it looks like a star.
DimensionName, category, brand
DimProduct
DimensionName, city, segment
DimCustomer
FactAmount, quantity
FactSales
DimensionStore, region
DimStore
DimensionDay, month, year
DimDate
Design 2 · Snowflake
What is a snowflake schema?
The same idea, but dimensions are normalised into sub-tables — the branches look like a snowflake.
Fact
FactSales
Links to DimProduct, DimCustomer, DimStore, DimDate.
Dimension
DimProduct
Holds product name and a CategoryKey.
Sub-dimension
DimCategory → DimDepartment
Category and department details live in their own tables.
Side by side
Key differences
↔️ Swipe the table sideways
PointStar schemaSnowflake schemaDimension designDenormalised — one table per dimensionNormalised — split into sub-tablesNumber of joinsFewerMoreQuery speedUsually fasterCan be slowerStorageSome duplicate valuesLess duplicationEase of useSimple to understandMore complexMaintenanceUpdates may touch repeated valuesUpdates in one placeBest forBI and dashboardsLarge, complex hierarchies
🧭 Want to model a real sales dataset as a star schema? Our trainers do it step by step in live labs.
One extra join per level of hierarchy — that’s why star schemas are usually faster and simpler for reports.
Decide
Which one should you use?
⭐
Choose star for…
Most reporting and BI
Power BI and Microsoft Fabric semantic models (Microsoft recommends star)
Simple, fast dashboards
Default choice
❄️
Choose snowflake when…
Dimensions are very large
Hierarchies are deep
Dimensions are shared across many fact tables and duplication is a real problem
Special cases
Many teams use a mostly-star design and snowflake only a few dimensions.
In Azure
Building star and snowflake schemas in Azure
You can build either design in Azure Synapse, Azure SQL Database, Databricks or a Microsoft Fabric Warehouse/Lakehouse. Data engineers usually land raw data in the lake (Bronze), clean it (Silver) and then build star-schema Gold tables for reporting. Learn more in data modelling in Azure and Medallion architecture.
✕ Snowflaking every dimension “to save space”
✕ Putting text descriptions inside the fact table
✕ Missing a proper date dimension
✕ No surrogate keys on dimensions
Learn data modelling hands-on Our Azure Data Engineer training includes star-schema modelling in Synapse, Fabric and Power BI with real projects.
You can learn Snowflake Schema vs Star Schema in our live classroom batches near JNTU/KPHB or in live online batches with Azure Trainings. Book a free demo class to see how we teach before you join.
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