Data warehouse design methods consider the read-oriented characteristic of warehouse data and enable the efficient query processing over large amounts of data. A special type of relational database schemas, called star schema, is often used to model the multiple dimensions of warehouse data (in contrast to the two-dimensional representation of normal relational schemas). In this case, the database consists of a central fact table and several dimension tables. The fact table contains tuples that represent business facts (measures) to be analyzed. Each fact table tuple references multiple dimensional table tuples each one representing a dimension of interest. Since dimension tables are not normalized, joining the fact table with the dimension tables provides different dimensional views of the warehouse data in an efficient way. A variant of the star schema, called the 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), roll-up and drill-down (increasing and decreasing the level of aggregation) have been proposed in this context. For the implementation of multidimensional databases, there are two main approaches:

In the first approach, extended relational DBMSs, called relational OLAP (ROLAP) servers, use a relational database to implement the multidimensional model and operations. ROLAP servers provide SQL extensions and translate data cube operations to relational queries.

In the second approach, multidimensional OLAP (MOLAP) servers store multidimensional data in non-relational specialized storage structures. These systems usually pre-compute the results of complex operations in order to increase performance.