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Linking and Embedding Database) by Microsoft and JDBC (Java Database Connec-
tion). This tier also contains a metadata repository, which stores information about
the data warehouse and its contents. The metadata repository is further described in
Section 4.1.7.
2. The middle tier is an OLAP server that is typically implemented using either (1) a
relational OLAP (ROLAP) model (i.e., an extended relational DBMS that maps oper-
ations on multidimensional data to standard relational operations); or (2) a multi-
dimensional OLAP (MOLAP) model (i.e., a special-purpose server that directly
implements multidimensional data and operations). OLAP servers are discussed in
Section 4.4.4.
3. The top tier is a front-end client layer, which contains query and reporting tools,
analysis tools, and/or data mining tools (e.g., trend analysis, prediction, and so on).

4.1.5 Data Warehouse Models: Enterprise Warehouse,
Data Mart, and Virtual Warehouse
From the architecture point of view, there are three data warehouse models: the
enterprise warehouse, the data mart, and the virtual warehouse.

Enterprise warehouse: An enterprise warehouse collects all of the information about
subjects spanning the entire organization. It provides corporate-wide data inte-
gration, 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 imple-
mented on traditional mainframes, computer superservers, or parallel architecture
platforms. It requires extensive business modeling and may take years to design
and build.
Data mart: 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 exam-
ple, a marketing data mart may confine its subjects to 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 Windows 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.