ETLELTpipelines

ETLELT pipelines are data integration workflows that blend ETL (extract, transform, load) and ELT (extract, load, transform) approaches within a single process. In an ETLELT pipeline, data may be transformed before loading into the target data store and/or transformed after loading, depending on data characteristics, platform capabilities, and governance requirements. The term reflects a hybrid strategy that leverages both pre-load processing and in-database or in-warehouse transformations to optimize performance and data quality.

Architecture and workflow: Data sources feed into an ingestion layer. A pre-load transformation stage can cleanse, normalize, and enrich data before it is loaded into the target (for example a data warehouse or data lake). After loading, post-load transformations run inside the target environment, taking advantage of its compute, indexing, and analytics features. Orchestration coordinates extraction, load, and transformation steps, and may route data through multiple paths for different subjects or domains. The approach is often used when high volumes require early filtering while preserving the ability to perform sophisticated transformations in the warehouse.

Use cases and benefits: ETLELT pipelines are common during data warehouse modernization, cloud migrations, and multi-source integrations. They offer flexibility to implement urgent cleansing outside the warehouse while enabling scalable, maintainable transformations inside the warehouse via specialized SQL or data modeling tools. Benefits include improved performance for large datasets, better governance through staged processing, and reuse of existing ETL or ELT components.

Challenges and considerations: Balancing transformation boundaries, cost management of dual processing, data lineage, and monitoring across stages. Tooling typically includes data integration platforms, orchestration systems, and post-load tooling like dbt or warehouse-native features. The exact division between ETL and ELT steps is driven by data volume, latency requirements, and the capabilities of the target platform.