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When should you use a STAR and when a SNOW-FLAKE schema?
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Answer Posted / freak.abinitio
Snowflake schema architecture is a more complex variation of a star schema design. The main difference is that dimensional tables in a snowflake schema are normalized, so they have a typical relational database design.
Snowflake schemas are generally used when a dimensional table becomes very big and when a star schema can’t represent the complexity of a data structure. For example if a PRODUCT dimension table contains millions of rows, the use of snowflake schemas should significantly improve performance by moving out some data to other table (with BRANDS for instance).
The problem is that the more normalized the dimension table is, the more complicated SQL joins must be issued to query them. This is because in order for a query to be answered, many tables need to be joined and aggregates generated.
A dimensional modelling technique in which a detail fact table is linked to dimension tables.
The data in data warehouses and data marts is accessed by end-users. The information contained in the data warehouse/data mart must be easy for the end-user to use and access. Denormalized designs are easier for end-users to use than highly normalized designs, however these designs are more difficult to design and maintain.
The Star Schema diagram graphically models the end-user's view (i.e., the denormalized view) of how the information is accessed.
Components of a Star Schema Diagram
The diagram has three main components:
· Fact Table and its contents: metric attributes and the foreign keys necessary to join to the dimension tables,
· Dimension Tables and their contents: reference attributes, hierarchical attributes, and metric attributes. The dimension tables are highly denormalized,
· the lines that link the Dimension Tables to the Fact Table.
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