Alkis Simitsis (National Technical University of Athens, Greece), Panos Vassiliadis (University of Ioannina, Greece) and Timos Sellis (National Technical University of Athens, Greece)
Copyright: © 2005
A data warehouse (DW) is a collection of technologies aimed at enabling the knowledge worker (executive, manager, analyst, etc.) to make better and faster decisions. The architecture of a DW exhibits various layers of data in which data from one layer are derived from data of the lower layer (see Figure 1). The operational databases, also called data sources, form the starting layer. They may consist of structured data stored in open database and legacy systems, or even in files. The central layer of the architecture is the global DW. The global DW keeps a historical record of data that result from the transformation, integration, and aggregation of detailed data found in the data sources. An auxiliary area of volatile data, data staging area (DSA) is employed for the purpose of data transformation, reconciliation, and cleaning. The next layer of data involves client warehouses, which contain highly aggregated data, directly derived from the global warehouse. There are various kinds of local warehouses, such as data mart or on-line analytical processing (OLAP) databases, which may use relational database systems or specific multidimensional data structures. The whole environment is described in terms of its components, metadata, and processes in a central metadata repository, located at the DW site.