Determine star or snowflake schema requirements

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After you determine your dimensions and attributes, you can create a logical model to represent the relationships to the business process you are trying to model.

Star schema
Dimensions identified only have a direct relationship with the central fact table. This relationship model is known as a star schema because it closely resembles a central core (fact table) with rays of light (dimension tables) emanating from its center.















Snowflake schema
In the snowflake model approach, the State dimension has “snowflaked” off the City dimension. This means that to get to the State dimension attributes, you now have to use City as an intermediate joined table. The more intermediate tables that are added, the more tables need to be joined to get to a particular attribute in the leaf level tables.













So, which schema is better? Both star schema and snowflake schema offer advantages over one another for different scenarios. There is no right or wrong choice, it all depends on business requirements, use cases, and other factors, which include volume and cardinality of the source data. In most cases, star schemas offer a better starting point because they are simpler to design, load, and maintain. In certain data warehouse designs you end up with what you can call “starflake” schemas, where you keep the structure a star schema as long as possible and only snowflake if absolutely necessary.

 Exam 70-767

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