ETL vs ELT: Data Pipeline Pattern Comparison

Explain the difference between ETL and ELT and when to use each.

ETL (Extract, Transform, Load)

Transform data before loading it into the target system (in a staging layer or ETL tool).

Flow: Source → Extract → Transform (staging) → Load

Pros: Only clean data enters the target; suitable for sensitive data (masking during transform).

Cons: Schema changes require re-runs; staging requires extra storage; less flexible.

ELT (Extract, Load, Transform)

Load raw data into the target system first (usually a cloud data warehouse), then transform in-place.

Flow: Source → Extract → Load (raw layer) → Transform (inside warehouse)

Pros: Raw data preserved for re-processing; leverages warehouse compute power; flexible and iterative.

Cons: Target stores large volumes of raw data; access control requires care.

Modern Trend

ELT is preferred in the cloud era (BigQuery, Snowflake, Redshift have strong compute). Combined with dbt for SQL-based transformations.