ETLELT

ETLELT is a term used in data engineering to describe a hybrid data integration paradigm that blends elements of ETL (extract, transform, load) and ELT (extract, load, transform). In ETLELT workflows, data may undergo transformations at multiple points in the pipeline, with some transforms applied before loading to enforce data quality or schema alignment, and additional transforms performed after loading within the target system when suitable. The approach is intended to leverage the strengths of both ETL and ELT to optimize data quality, governance, and processing efficiency across diverse data sources and storage platforms.

Process model and characteristics: In practice, ETLELT is not a single fixed sequence but a set of patterns where the choice of pre- or post-load transformations depends on data characteristics, target capabilities, and performance considerations. Typical configurations include applying selective pre-load cleaning and normalization, loading raw and curated datasets into a data lake or data warehouse, and executing heavy transformations inside the target engine using its compute resources. The approach relies on robust metadata management and data lineage to track transformation steps across stages, and is well suited to hybrid architectures that combine data lakes with scalable warehouses.

Tools and adoption: ETLELT is a descriptive term rather than a standardized technology, and it can be implemented with common data integration platforms, orchestration tools, and processing engines. It supports SQL, Spark, and Python-based transformations and often depends on metadata catalogs and lineage dashboards. Advocates argue that it offers flexibility and governance benefits, while critics note added complexity and potential latency if not managed carefully.

See also: ETL, ELT, data integration, data pipelines, and data governance.