SNOWFLAKE SCHEMA
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.
Fact Tables
sales fact table - contains all details regarding sales
orders fact table - in some cases the table can be split into open orders and historical orders. Sometimes the values for historical orders are stored in a sales fact table.
budget fact table - usually grouped by month and loaded once at the end of a year.
forecast fact table - usually grouped by month and loaded daily, weekly or monthly.
inventory fact table - report stocks, usually refreshed daily
orders fact table - in some cases the table can be split into open orders and historical orders. Sometimes the values for historical orders are stored in a sales fact table.
budget fact table - usually grouped by month and loaded once at the end of a year.
forecast fact table - usually grouped by month and loaded daily, weekly or monthly.
inventory fact table - report stocks, usually refreshed daily
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