While data-driven approaches somehow simplify the design of ETL (extraction, transformation,
and loading), since each data in the DW is rooted in one or more attributes
Conceptual Modeling Solutions for the Data Warehouse
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of the sources, they give user requirements a secondary role in determining the information
contents for analysis, and give the designer little support in identifying facts,
dimensions, and measures. Conversely, requirement-driven approaches bring user
requirements to the foreground, but require a larger effort when designing ETL.
Data-Driven.Approaches
Data-driven approaches are feasible when all of the following are true: (1) detailed
knowledge of data sources is available a priori or easily achievable; (2) the source
schemata exhibit a good degree of normalization; (3) the complexity of source
schemata is not high. In practice, when the chosen architecture for the DW relies
on a reconciled level (or operational data store) these requirements are largely satisfied:
in fact, normalization and detailed knowledge are guaranteed by the source
integration process. The same holds, thanks to a careful source recognition activity,
in the frequent case when the source is a single relational database, well-designed
and not very large.
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