ETL vs. ELT vs. ETLT: What’s the Real Difference?

Here is the distinct function of each approach based on modern architecture needs 👇

📌 1. ETL (Extract, Transform, Load) — "The Classic"
Process: Data is extracted ➡ Transformed in a separate staging server ➡ Loaded into the Warehouse.
Best For: Complex transformations, strict security/compliance masking before data lands, or legacy on-prem systems with limited compute.

☁️ 2. ELT (Extract, Load, Transform) — "The Modern Standard"
Process: Extract raw data ➡ Load immediately into the Warehouse ➡ Transform using SQL/dbt inside the warehouse.
Best For: Modern Cloud Data Warehouses (Snowflake, BigQuery, Redshift) where storage is cheap and compute is massive.

⚖️ 3. ETLT (Extract, Transform, Load, Transform) — "The Hybrid"
Process: Lightweight cleaning (PII masking) before loading ➡ Heavy analytics transformations after loading.
Best For: When you need both strict Data Quality checks (pre-load) and complex analytical modeling (post-load).