When building semantic models, one of the most important decisions is choosing a star schema. This simple yet powerful structure centers around a fact table, surrounded by descriptive dimension tables. Widely adopted, the star schema improves clarity, boosts performance, and easily scales for large datasets .
What are Dimension Tables?
Dimension tables add context to the facts—the “who, what, where, when” of your data.
- Examples include Customer, Product, Date, or Region .
- These tables usually have fewer rows, with multiple columns describing attributes.
What are Fact Tables?
Fact tables store the numeric measures for analysis, such as sales amount, order quantity, or hours worked .
- They often have many rows and link to dimension tables through key columns (e.g., CustomerID).
- Granularity matters: Does each row represent a single transaction, a day, or a customer? Defining this keeps analysis accurate.
Understanding Relationships
Facts and dimensions are connected through one-to-many relationships:
- One customer may link to many sales transactions.
- One product might appear in numerous sales records .
- The “one” side is always on the dimension table, while “many” is the fact table side.
Filtering by a dimension instantly narrows down relevant facts.
Working with Multiple Fact Tables
Most real-world models use several fact tables—like Sales, Inventory, or Finance.
- These can share common dimension tables such as Date or Product .
- Filtering by a single dimension (for example, choosing a product) applies to all related fact tables, enabling side-by-side analysis.
Star Schema vs. Snowflake Schema
Star schemas keep dimensions as simple, flat tables.
- Snowflake schemas connect dimensions to other dimensions (like Product to Category) .
- Though snowflake schemas cut duplication, they result in complex and slower models.
Power BI typically recommends the star schema for best results.
Keys: Natural vs. Surrogate
Relationships use keys to connect tables:
- Natural keys come straight from the source system (for example, product_code).
- These do not always guarantee uniqueness, especially with historical tracking.
- Surrogate keys—unique identifiers generated during the ETL process—ensure each dimension row is distinct. Fact tables reference these surrogate keys, which supports historical records and avoids conflicts .
Why Choose Star Schema?
A star schema delivers the perfect balance of simplicity, performance, and scalability:
- Fact tables hold the numbers.
- Dimension tables provide context.
- Relationships tie it all together .
This proven design makes your model easy to understand and efficient to use.
For further details about semantic modeling or Power BI best practices, contact us or explore our resources.
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