Definition · data pipelines
ELT
ELT is a data integration pattern that loads raw data into the destination first and performs transformations inside the warehouse, enabled by cheap cloud compute. For ELT, a useful definition states a data integration pattern that loads raw data into the destination first and performs transformations inside the warehouse, who owns it, and which decision it supports.
Also known as extract load transform, extract, load, transform
Why it matters
Understanding ELT matters because leaders need a shared, source-backed meaning before they can compare results, explain performance, or decide what to do next. Pluvo follows an ELT-style pattern: it loads source data into a columnar store, then models and computes over it with a semantic layer and ontology rather than pre-transforming everything up front.
In practice
Operating example
ELT is useful when teams need a shared interpretation of a data integration pattern that loads raw data into the destination first and performs transformations inside the warehouse, enabled by cheap cloud compute. The definition should make source data, timing, ownership, and the decision it supports explicit.
Pluvo example
Pluvo follows an ELT-style pattern: it loads source data into a columnar store, then models and computes over it with a semantic layer and ontology rather than pre-transforming everything up front.
In practice, teams should define ELT with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding ELT matters because leaders need a shared, source-backed meaning before they can compare results, explain performance, or decide what to do next. Pluvo follows an ELT-style pattern: it loads source data into a columnar store, then models and computes over it with a semantic layer and ontology rather than pre-transforming everything up front.
A strong workflow for ELT 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.
Pluvo follows an ELT-style pattern: it loads source data into a columnar store, then models and computes over it with a semantic layer and ontology rather than pre-transforming everything up front.
FAQ
What is ELT?
ELT is a data integration pattern that loads raw data into the destination first and performs transformations inside the warehouse, enabled by cheap cloud compute. For ELT, a useful definition states a data integration pattern that loads raw data into the destination first and performs transformations inside the warehouse, who owns it, and which decision it supports.
Why has ELT replaced ETL in modern stacks?
Understanding ELT matters because leaders need a shared, source-backed meaning before they can compare results, explain performance, or decide what to do next. Pluvo follows an ELT-style pattern: it loads source data into a columnar store, then models and computes over it with a semantic layer and ontology rather than pre-transforming everything up front.
What is the difference between ETL and ELT?
The boundary for ELT differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers a data integration pattern that loads raw data into the destination first and performs transformations inside the warehouse, enabled by cheap cloud compute, so teams should compare those boundaries before using it in reporting or planning.