Star and snowflake schemas are both data modeling techniques used to structure data in a relational database. However, there are key differences between them:
Star Schema:
- A star schema consists of a single fact table that is surrounded by dimension tables.
- The fact table contains the measures (numeric values) that we want to analyze, while the dimension tables contain descriptive attributes of the measures.
- In a star schema, the dimension tables are connected to the fact table through a series of relationships represented by foreign keys.
- Star schemas are optimized for queries that aggregate data along several dimensions, making them ideal for use in business intelligence and data warehousing systems.
Star Schema
Snowflake Schema:
- A snowflake schema is a type of star schema, but with more normalized dimension tables.
- In a snowflake schema, the dimension tables are not only connected to the fact table but also to other dimension tables. This normalization helps to reduce data redundancy and maintain data integrity.
- The normalization process makes snowflake schemas more complex than star schemas, but it also allows for more flexible data modeling.
- Snowflake schemas are well-suited for transactional databases and data marts where detailed data analysis is not a priority.
Snowflake Schema