ELT stands for Extract, Load, Transform—a data integration pattern where raw data is first loaded into a central system such as a data warehouse and then transformed inside that system. This differs from ETL, which transforms data before loading. ELT takes advantage of the high performance and scalability of cloud warehouses to perform transformations efficiently.
Why it matters
ELT simplifies data ingestion by reducing the number of moving parts in the pipeline. It is particularly suited for large datasets and analytics workflows where transformation logic benefits from the compute power of modern warehouses. ELT improves agility when schema or transformation rules change.
Examples
Loading raw event logs into BigQuery and then running SQL scripts to normalize and join the data is an ELT workflow. The lesson ETL and ELT Pipelines compares both approaches.