Definition · data pipelines
ETL
ETL is a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads it into a destination such as a warehouse. For ETL, a useful definition states a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads, who owns it, and which decision it supports.
Also known as extract transform load, extract, transform, load
Why it matters
Understanding ETL matters because leaders need a shared, source-backed meaning before they can compare results, explain performance, or decide what to do next. When the term is tied to a source system, owner, and review cadence, it becomes easier to audit assumptions, catch changes early, and keep operators aligned.
In practice
Operating example
ETL is useful when teams need a shared interpretation of a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads it into a destination such as a warehouse. The definition should make source data, timing, ownership, and the decision it supports explicit.
Review example
ETL should be reviewed whenever the source system, calculation logic, time period, or decision owner changes. That keeps the definition useful instead of letting it drift into a label.
In practice, teams should define ETL with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding ETL matters because leaders need a shared, source-backed meaning before they can compare results, explain performance, or decide what to do next. When the term is tied to a source system, owner, and review cadence, it becomes easier to audit assumptions, catch changes early, and keep operators aligned.
A strong workflow for ETL separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
FAQ
What is ETL?
ETL is a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads it into a destination such as a warehouse. For ETL, a useful definition states a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads, who owns it, and which decision it supports.
What is the difference between ETL and ELT?
The boundary for ETL differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads it into a destination such as a warehouse, so teams should compare those boundaries before using it in reporting or planning.
What does ETL stand for?
ETL is a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads it into a destination such as a warehouse. For ETL, a useful definition states a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads, who owns it, and which decision it supports. For ETL, the practical boundary is a data integration pattern that extracts data from sources, transforms it into a target structure, and then loads it into a destination such as a warehouse.