ELT (Extract, Load, Transform) is a data integration pattern in which raw data is first extracted from its sources and loaded into a target system such as a data warehouse or lakehouse, and only then transformed inside that system. It reverses the order of the older ETL approach, which transforms data before loading it.
The shift became practical once cloud warehouses made in-place compute cheap and scalable, so teams load raw data quickly and run transformations with SQL where the data already sits. For example, a company might load raw event logs into its warehouse overnight, then build cleaned, aggregated tables from them during the day.
ELT decides where and when data is transformed. Whether the transformed output is actually usable in a given AI run, and whether that run can be reproduced later, is a separate readiness question.