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What are different types of data warehouse models, from the architecture point of view?

Discussion in brief various types of the data warehouse.

From an architecture point of view, there are three data warehouse models. They are as follows –

Enterprise Warehouse –

An enterprise warehouse collects all of the information about subjects spanning the entire organization. It provides corporate-wide data integration, usually from one or more operational systems or external information providers, and is cross-functional in scope. It typically contains detailed data as well as summarized data and can range in size from a few gigabytes to hundreds of gigabytes, terabytes, or beyond. An enterprise data warehouse may be implemented on traditional mainframes, compute. super servers, or parallel architecture platforms. It requires extensive business modeling and may take years to design and build.


A-data mart contains a subset of corporate-wide data that is of value to a specific group of users. The scope is confined to specific selected subjects. For example, a marketing data mart may confine its subjects to the customer, item, and sales. The data. contained in data marts tend to be summarized.

Data marts are usually implemented on low-cost departmental servers that are UNIX/LINUX or Window-based. The implementation cycle of a data mart is more likely to be measured in weeks rather than months or years. However, it may involve complex integration in the long run if its design and planning were not. enterprise-wide:

Depending on the source of data, data marts can be categorized as independent or dependent. Independent data marts are sourced from data captured from one or more operational systems or external information providers, or from data generated locally within a particular department or ‘geographic area. Dependent data marts are sourced directly from enterprise data warehouses.

Virtual warehouse

A virtual warehouse is a set of views over oPerational.databases: For efficient query processing, only some of the possible summary views may be materialized. A virtual warehouse is easy to build but requires excess capacity on operational database servers.

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