by dimension tables include: Time dimension tables describe time at the lowest level of time granularity for which events are recorded in the star schema Geography dimension tables describe location data, such as country, state, or city Product dimension tables describe products Employee dimension tables describe employees, such as sales people Range dimension tables describe ranges of time, dollar values or other measurable quantities to simplify reporting Dimension tables are generally assigned a surrogate primary key, usually a single-column integer data type, mapped to the combination of dimension attributes that form the natural key. Benefits Star schemas are denormalized, meaning the typical rules of normalization applied to transactional relational databases are relaxed during star-schema design and implementation. The benefits of star-schema denormalization are: Simpler queries – star-schema join-logic is generally simpler than the join logic required to retrieve data from a highly normalized transactional schema. Simplified business reporting logic – when compared to highly normalized schemas, the star schema simplifies common business reporting logic, such as period-over-period and as-of reporting. Query performance gains – star schemas can provide performance enhancements for read-only reporting applications when compared to highly normalized schemas. Fast aggregations – the simpler queries against a star schema can result in improved performance for aggregation operations. Feeding cubes – star schemas are used by all OLAP systems to build proprietary OLAP cubes efficiently; in fact, most major OLAP systems provide a ROLAP mode of operation which can use a star schema directly as a source without building a proprietary cube structure. Disadvantages The main disadvantage of the star schema is that it's not as flexible in terms of analytical needs as a normalized data model. Normalized models allow any kind of analytical query to be executed, so long as it follows the business logic defined in the model. Star schemas tend to be more purpose-built toward a particular view of the data, thus not really allowing more complex analytics. Star schemas don't easily support many-to-many relationships between business entities. Typically these relationships are simplified in a star schema in order to conform to the simple dimensional model. Another disadvantage is that data integrity is not well-enforced due to its denormalized state. One-off inserts and updates can result in data anomalies, which normalized schemas are designed to avoid. Generally speaking, star schemas are loaded in a highly controlled fashion via batch processing or near real-time "trickle feeds", to compensate