Traditional ETL transforms data before loading into warehouses. Modern cloud warehouses with massive compute enable ELT—loading raw data then transforming within the warehouse. This shift changes how teams build and manage data pipelines.
ELT Advantages
ELT preserves raw data enabling future transformations without re-extraction. Warehouse compute scales transformation without separate infrastructure. SQL transformations are accessible to more team members than ETL code. Schema changes require updating transformations rather than extraction.
- ELT simplifies pipeline architecture with fewer moving parts
- Raw data preservation enables new analyses without re-extraction
- dbt has emerged as the standard tool for ELT transformations
- Modern warehouses handle transformation compute efficiently
- Consider data sensitivity—raw loading may include data requiring masking
When ETL Remains Relevant
ETL suits scenarios requiring transformation before storage—data masking for compliance, format conversion for incompatible sources, or aggregation reducing storage costs. Hybrid approaches apply ETL where needed while leveraging ELT for most transformations.