Slowly changing dimensions may be classified into three different types based on the nature of the changes. Type 1 relates to corrections, Type 2 to preservation of history, and Type 3 to soft revisions. Applying each type of revision to the data warehouse is different.
Large dimension tables such as customer or product need special considerations for applying optimizing techniques.
"Snowflaking" or creating a snowflake schema is a method of normalizing the STAR schema. Although some conditions justify the snowflake schema, it is generally not recommended.
Miscellaneous flags and textual data are thrown together in one table called a junk dimension table.
Aggregate or summary tables improve performance. Formulate a strategy for building aggregate tables.
A set of related STAR schemas make up a family of STARS. Examples are snapshot and transaction tables, core and custom tables, and tables supporting a value chain or a value circle. A family of STARS relies on conformed dimension tables and standardized fact tables.