For decades, ETL (Extract, Transform, Load) was the default for moving and cleaning enterprise data. But cloud-native platforms changed the rules, making ELT (Extract, Load, Transform) increasingly dominant.
In 2025, teams must decide: ETL or ELT? The wrong choice can inflate costs, slow down analytics, or create compliance risks. Understanding this decision is especially critical when planning zero-downtime cloud migrations where pipeline architecture directly impacts cutover success.
What Is ETL?
- Extract: Pull data from sources (databases, APIs, flat files).
- Transform: Apply rules, business logic, or cleanup outside the target system.
- Load: Deliver the transformed data into the warehouse.
Strengths:
- Pre-cleaned data enters the warehouse.
- Good for regulated or sensitive workloads.
- Established ecosystem of tools (Informatica, Talend, Pentaho).
Limitations:
- Extra infrastructure costs.
- Slower to iterate for analytics teams.
What Is ELT?
- Extract: Pull raw data from sources.
- Load: Push raw data into the cloud warehouse.
- Transform: Apply transformations inside the warehouse using SQL or tools like dbt.
Strengths:
- Scales with the power of cloud compute.
- Flexible, schema-on-read modeling.
- Faster iteration with modular transformations.
Limitations:
- Raw data may increase storage costs.
- Governance complexity if transformations are not standardized.
Side-by-Side: ETL vs ELT in 2025
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