Data warehouse schemas

Two important Data warehouse schemas are
1)star schema
2)snowflake schema

Before knowing these schemas,we have to know facts and dimensions.Let me start with an example of retail sales analysis data warehouse.

sales transactions-Fact.
customer,locations,suppliers,products and time are Dimensions.
Fact data is the major database component of a typical data warehouse,constituting around 70% of total database volume.It represents a physical transaction that has occured at a point of time and can change during the life of a data warehouse.
Dimension data will change over time.This may be due to changes in buisness,such as regional reorganization.It is designed to minimize the cost of change and is very low volume data.

It means the central factual transaction table is fact table and the surrounding reference tables are dimension tables.Putting them together and can imagine them representing as star;hence the name star schema.It is used to model the multiple dimensions of warehouse data (in contrast to the two-dimensional representation of normal relational schemas).

A variant of the star schema, called snowflake schema , is commonly used to explicitly represent the dimensional hierarchies by normalizing the dimension tables.A more natural way to consider multidimensionality of warehouse data is provided by the multidimensional data model. Thereby, the data cube is the basic underlying modeling construct. Special operations like pivoting (rotate the cube), slicing- dicing (select a subset of the cube), drill-up and drill-down (increasing and decreasing the level of aggregation) have been proposed in this context.