ETL vs ELT for modern analytics stacks

ETL versus ELT is rarely a religious war. Mature teams push light cleansing early and heavy business transforms close to the warehouse where compute is elastic.

Use ELT when the warehouse is the compute engine

Landing raw data and transforming with SQL or dbt-style models keeps logic versioned and reviewable next to your BI models.

Keep ETL for constrained sources

APIs with strict rate limits, PII redaction needs, or on-prem extracts may still need transform-before-load.

Standardize observability either way

Whether ETL or ELT, log row counts, durations, and data tests. The pattern matters less than knowing when loads fail.

Designing pipelines for a modern stack? Contact BI Forge.