Dimension Modeling Methodologies:
The below are most used methodologies that are used for building a data mart.
Data warehouse schemas:
Collection of database objects, including tables, views, indexes, and synonyms are termed as schema.
When you have identified your dimension and fact tables now it is needed to relate them, star schema methodology bound these tables in such a way that the fact table is kept center surrounded by any number of dimension tables representing a star style and called star schema. The Fact table is normally in the third normal form because the dependency is either on one dimension or all of them but not on combination of few dimensions.
The reason to choose a star schema is its simplicity from the users’ point of view: queries are never complex because the only joins and conditions involve a fact table and a single level of dimension tables, without the indirect dependencies.
A Kind of Star schema that is more complex data warehouse schema option to choose than its predecessor, It is called a snowflake schema because the diagram of the schema resembles a snowflake. The focus of this schema is to normalize the data warehouse to reduce the query performance, the stated schema normalized a dimension table into more sub tables although it saves space as it increases the dimension tables and require more foreign key to join tables but leaving the extraction process little bit complex as it increases the usage of complex SQL queries.