ETL vs ELT: Data Pipeline Approaches Explained
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the two primary approaches to moving data from source systems into analytical environments. Understanding the difference helps you choose the right architecture for your data needs.
ETL: Extract, Transform, Load
In ETL, data is extracted from source systems, transformed (cleaned, restructured, enriched) in an intermediate processing layer, and then loaded into the target data warehouse. ETL was the dominant approach when data warehouse storage and compute were expensive — you transformed data before loading to minimise warehouse resource usage.
ELT: Extract, Load, Transform
In ELT, data is extracted from source systems and loaded directly into the data warehouse in raw form. Transformation happens inside the warehouse using SQL (typically with dbt). ELT has become the dominant approach for cloud data warehouses because:
- Modern cloud warehouses have cheap, scalable storage — loading raw data is affordable
- Compute power for transformation is elastic and cost-effective in the cloud
- Raw data is preserved — you can redefine transformations retroactively
- Transformation logic is centralised in the warehouse — easier to version, test, and document
Tools for Each Approach
- ELT ingestion: Fivetran, Stitch, Airbyte, Meltano, custom scripts
- Transformation (ELT): dbt (data build tool) — the industry standard for SQL-based transformation
- Traditional ETL: AWS Glue, Azure Data Factory, Informatica, Talend
Our Recommendation
For most clients, ELT with a cloud data warehouse and dbt is the right approach — it is flexible, maintainable, and aligns with the direction of the data engineering industry.