The key difference is when transformation happens. In ETL, you clean and reshape the data before it enters the warehouse. In ELT, you load raw data first and transform it inside the warehouse using SQL.
Why ELT has become the default in modern data engineering
Cloud data warehouses — Snowflake, BigQuery, Redshift, Databricks — are designed to process enormous amounts of data very efficiently. When you can run complex SQL transformations on a system with near-unlimited compute, it no longer makes sense to do expensive preprocessing outside the warehouse before loading.
ELT also preserves the raw data. If your transformation logic has a bug, the raw data is still there in the warehouse. You fix the transformation and re-run it. With ETL, a bug in the transformation layer means you may need to re-extract from the source — which is slower and more disruptive.
Tools like dbt (data build tool) have accelerated ELT adoption by making SQL-based transformations modular, testable, and version-controlled — bringing software engineering practices to data transformation for the first time.
When ETL still makes sense
ETL is still appropriate when data contains sensitive information that should be masked before entering any storage layer, when data volume is small and a lightweight processing layer is sufficient, or when working with older systems where ELT is not practical. Legacy enterprise environments in particular often still run traditional ETL pipelines, so knowing both approaches remains important.
In practice, most modern data engineering roles expect you to understand both — and to know when each approach is appropriate based on the system constraints.