ETL vs ELT: Where the Transformation Happens
The difference between transforming before or after loading, why cloud warehouses pushed teams to ELT, and when ETL still wins.
ETL / ELTBeginner2 min readUpdated 11 Oct 2026
ETL / ELTThe difference is the order
ETL: source → extract → TRANSFORM (Spark, Python) → load → warehouse
ELT: source → extract → load raw → TRANSFORM inside the warehouse (SQL, dbt)Why ELT became popular
- Cloud warehouses separate storage and compute, so storing raw data is cheap and scaling SQL is easy.
- Raw data stays available, so transformations can be rebuilt after a logic change.
- SQL and tools like dbt let analysts own transformations with tests and version control.
When ETL still makes sense
| Situation | Why transform first |
|---|---|
| Sensitive fields (PII) | Mask or drop before it lands anywhere |
| Huge, messy or unstructured data | Spark is cheaper for parsing and heavy cleaning |
| Strict contracts | Reject bad data before it reaches consumers |
| Non-SQL logic | ML features, complex parsing, external API enrichment |
In practice: both
Most modern pipelines are hybrid: a light ETL step (validate, mask, convert to Parquet), then ELT in the warehouse for modeling.
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