Relational Databases Relational databases are the most popular platform for supporting data warehouses and data marts. The existence of SQL as a relatively standard query language and ODBC as a common access method has enabled the proliferation of relational databases for analytic applications. Since relational databases can leverage the parallel processing capabilities afforded by highly scalable multiprocessing UNIX platforms, companies are using these data management systems to build terabyte-capacity data warehouses and staging areas. A staging area is where companies can load cleansed and integrated operational data before heavily transforming them into specialized data marts. Unlike relational databases, multidimensional databases don't store data in tables, rows, and columns.
Multidimensional databases are akin to spreadsheets, storing data in a matrix-like structure.
In most cases, multidimensional databases are used to support application-specific data marts. There are a number of databases that employ specialized index structures, such as bitmaps, to speed responses to complex queries. In addition, there are compression and parallelization tools on the market today that companies can use to dramatically speed up the performance of relational databases. A data warehouse engine requires a supporting cast of tools to maintain performance and an operational environment. Tools and utilities, such as back up, performance management, and database accelerators, become critical to the success of a data warehouse as the data warehouse becomes a true operational or production system within a company.