In this diagram, all areas are directly connected to the backbone. In the rare situations where a new area is introduced that cannot have a direct physical access to the backbone, a virtual link has to be configured.
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In this example, area 1 does not have a direct physical connection into area 0. A virtual link has to be configured between RTA and RTB. Area 2 is to be used as a transit area and RTB is the entry point into area 0.
A data mart serves the same role as a data warehouse, but it is intentionally limited in scope. It may serve one particular department or line of business. The advantage of a data mart versus a data warehouse is that it can be created much faster due to its limited coverage. However, data marts also create problems with inconsistency. It takes tight discipline to keep data and calculation definitions consistent across data marts. This problem has been widely recognized, so data marts exist in two styles. Independent data marts are those which are fed directly from source data. They can turn into islands of inconsistent information. Dependent data marts are fed from an existing data warehouse. Dependent data marts can avoid the problems of inconsistency, but they require that an enterprise-level data warehouse already exist.
A data warehouse is updated on a regular basis by the ETL process (run nightly or weekly) using bulk data modification techniques. The end users of a data warehouse do not directly update the data warehouse except when using analytical tools, such as data mining, to make predictions with associated probabilities, assign customers to market segments, and develop customer profiles. 2ff7e9595c
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