Many methodologies have been proposed to simplify the information technology efforts required to support the data warehousing process on an ongoing basis. This has led to debates about the best architecture for delivering data warehouses in organizations.
Two basic types of data warehouse architecture exist: enterprise data warehouses and data marts.
The enterprise data warehouse contains enterprise-wide information integrated from multiple operational data sources for consolidated data analysis. Typically, it is composed of several subject areas, such as customers, products, and sales, and is used for both tactical and strategic decision making. The enterprise data warehouse contains both detailed point-in-time data and summarized information, and can range in size from 50 gigabytes (GB) to more than 1 terabyte. Enterprise data warehouses can be very expensive and time-consuming to build and manage. They are usually created from the top down by centralized information services organizations.
The data mart contains a subset of enterprise-wide data that is built for use by an individual department or division in an organization. Unlike the enterprise data warehouse, the data mart is usually built from the bottom up by departmental resources for a specific decision-support application or group of users. Data marts contain summarized and often detailed data about a subject area. The information in the data mart can be a subset of an enterprise data warehouse (dependent data mart) or can come directly from the operational data sources (independent data mart).
Enterprise data warehouses and data marts are constructed and maintained through the same iterative process described earlier. Furthermore, both approaches share a similar set of technological components.