Enormous amounts of data, disparate legacy systems and nonstandardized business terminology are just a few of the challenges an organization must overcome when building a data warehouse. In a typical data warehouse implementation, these challenges are addressed during extract, transform and load (ETL) development. ETL development is no small undertaking and is a sizeable portion of a data warehouse project. Companies often budget 40 to 70 percent of project development effort for their ETL tasks. Designing a robust and scalable ETL architecture is a critical factor for ensuring overall project success.
Because ETL components are the core to most, if not all, data warehouse implementations, it is not difficult to find companies whose greatest concern and greatest source of frustration is directly related to these tasks. Managers for data warehouse implementations often ask these questions:
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