Data Engineering Guide

ETL vs ELT

The ETL vs ELT debate is settled for most teams: ELT wins in the cloud era. But the nuances matter. This guide covers when each pattern fits, performance trade-offs, and a decision framework.

Diagram Your PipelinePipeline Documentation
12 min readFor Data Engineers & Architects

See it as a diagram

Everything below, as a diagram you can edit. Describe yours and see it in seconds.

93/2000• 3 credits left
Try:

No account needed · Editable canvas, not a picture

ETL vs ELT at a Glance

DimensionETLELT
Transform Where?Separate engine (Spark, Informatica)In the warehouse (SQL/dbt)
Raw Data Stored?No — transformed before loadingYes — raw data available
Compute CostSeparate infra (expensive)Warehouse compute (pay-per-query)
FlexibilityMust re-extract to change transformRe-transform raw data anytime
LatencyHigher (2 hops)Lower (direct load + async transform)
Primary ToolsInformatica, Talend, GlueFivetran + dbt, Airbyte + dbt

How ETL Works

Extract from Sources
Transform (Spark/ETL engine)
Load to Warehouse

Data is extracted from sources, transformed in a separate compute engine (Spark, Informatica), then loaded into the destination. The destination only sees cleaned data.

How ELT Works

Extract from Sources
Load Raw to Warehouse
Transform in Warehouse (dbt)

Data is extracted and loaded raw into the warehouse, then transformed using SQL (typically dbt). The warehouse's compute power handles transformations at scale.

Decision Framework

Choose ETL when:

  • PII must be scrubbed before storage
  • On-premise data warehouse (no elastic compute)
  • Complex transformations (ML, image processing)
  • Regulatory requirement to not store raw data

Choose ELT when:

  • Cloud warehouse (Snowflake, BigQuery, Redshift)
  • Want to reprocess raw data without re-extraction
  • SQL-based transformations (dbt, Dataform)
  • Need faster time-to-value for new data sources

The Modern EL+T Stack

Most modern teams use the EL+T pattern: extraction + loading is handled by one tool, transformation by another. This separation of concerns is the dominant architecture in 2026.

StageToolPurpose
Extract + LoadFivetran, Airbyte, MeltanoMove raw data to warehouse
Transformdbt, Dataform, SQLMeshModel, test, document in SQL
OrchestrateAirflow, Dagster, PrefectSchedule and monitor pipelines
WarehouseSnowflake, BigQuery, RedshiftStore and compute

FAQ

What is the difference between ETL and ELT?
ETL transforms data before loading. ELT loads raw data first, then transforms in the warehouse. ELT leverages cloud warehouse compute and is the dominant pattern for modern data stacks.
Is ELT better than ETL?
For cloud data stacks, generally yes. ELT is simpler, retains raw data, and leverages warehouse compute. ETL is still better for on-premise and when raw PII must not be stored.
What tools are used for ETL vs ELT?
ETL: Informatica, Talend, AWS Glue. ELT: Fivetran/Airbyte (extract+load) + dbt (transform) with Snowflake/BigQuery as the compute engine.

Diagram your ETL/ELT pipeline

AI-generated data pipeline diagrams with 1,200+ cloud icons. Map sources, transformations, and destinations.

Try Datadef Free