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Last updated Feb 9, 2024

ELT (Extract, Load, and Transform) represents a Data Integration methodology where data is first extracted (E) from source systems, then loaded (L) as raw data into a target system, followed by transformation (T) within the target. This approach, executed within the destination Data Warehouse, contrasts with the traditional ETL method, where data undergoes transformation prior to reaching its destination. For a comprehensive comparison, see ETL vs ELT.

The evolution from ETL to ELT has been propelled by the decreasing costs of cloud computing and storage, alongside the emergence of cloud-based data warehouses like Redshift, BigQuery, and Snowflake.

ELT is notably utilized in Data Lake environments. Airbyte emerged as a benchmark for open-source ELT in 2020, while Fivetran, being the pioneer, operates on a closed-source model.

# Connectors

For an extensive list of connectors, visit the Connector Catalog by Whaly.

Origin: Data Warehouse vs Data Lake | ETL vs ELT | ssp.sh
Created 2022-07-31