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Extract, Transform, and Load (ETL) in database usage and especially in data warehousing involves: * extracting data from outside sources; * transforming it to fit operational needs (which can include quality levels); and ultimately * loading it into the end target (database or data warehouse) The advantages of efficient and consistent databases make ETL very important as the way data actually gets loaded. This article discusses ETL in the context of a data warehouse, whereas the term ETL can in fact refer to a process that loads any database. ETL can also function to integrate contemporary data with legacy systems. Usually ETL implementations store an audit trail on positive and negative process runs. In almost all designs, this audit trail does not give the level of granularity which would allow a DBA to reproduce the ETL's result in the absence of the raw data. |
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