ELTprocess

The ELT process, standing for Extract, Load, Transform, is a data integration approach in which data is first extracted from source systems, loaded in its raw form into a target data store such as a data warehouse or data lake, and then transformed within the target environment. This contrasts with traditional ETL, where data is transformed before loading.

In an ELT workflow, connectors or extract pipelines pull data from transactional systems, logs, SaaS applications, and files. The raw data is loaded into the destination, often a cloud-based data warehouse or lakehouse, and transformations are performed using the warehouse’s compute resources. Transformations are typically implemented with SQL-based pipelines, and may be orchestrated alongside other data processing steps using tools such as dbt, Airflow, or other workflow managers.

ELT is well-suited to cloud-native architectures that provide scalable storage and compute. By loading raw data first, organizations retain flexibility to re-process data, support semi-structured and evolving schemas, and reduce data movement. It often enables faster ingestion of large data volumes and supports modern analytics, data science, and machine learning workloads.

Key considerations include data governance, lineage, and quality controls, since more data is retained in the target system. The approach can place greater demand on the destination's processing capacity and require strong monitoring to manage performance and costs. Typical use cases include analytics data platforms, data lakehouse implementations, and scenarios requiring rapid ingestion of diverse data sources.

Tools commonly used with ELT include data integration platforms, SQL-based transformation tools such as dbt, and orchestration systems like Airflow or Prefect, often operating in conjunction with cloud data warehouses such as Snowflake, BigQuery, or Redshift.