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.
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
| Factor | ETL | ELT |
|---|---|---|
| Where transformations run | ETL server | Cloud warehouse |
| Cost model | ETL infra + warehouse | Warehouse-only |
| Flexibility | Fixed schema | Iterative, agile |
| Compliance fit | Strong for sensitive data | Needs governance |
| Best used with | Legacy RDBMS, hybrid clouds | Snowflake, BigQuery, Synapse |
Real-World Cloud Scenarios
Snowflake
- ELT with dbt dominates, leveraging Snowflake’s scalable compute.
- ETL is still used for regulated financial workloads.
BigQuery
- Best suited for ELT pipelines due to its serverless model.
- Streaming ingestion pairs well with ELT.
Azure Synapse
- Hybrid ETL + ELT is common, especially when paired with Azure Data Factory.
Tools That Enable Both
- ETL Engines: Informatica, Talend, Matillion.
- ELT Pipelines: dbt, Fivetran, Airbyte.
- Hybrid Orchestration: Airflow, Prefect, Dagster.
When to Use ETL vs ELT
- Choose ETL when compliance, encryption, or pre-processing is mandatory.
- Choose ELT when speed, agility, and modern analytics are priorities.
- Choose Hybrid when migrating legacy systems into cloud warehouses.
Future of ETL vs ELT
Expect AI-assisted pipeline orchestration in the next 2–3 years, blending ETL and ELT decisions dynamically based on cost, latency, and compliance.
Conclusion
ETL and ELT both have their place in modern cloud architectures. The right strategy depends on your workloads, compliance needs, and long-term analytics goals.
When migrating, our ETL Data Migration Services design cloud-native pipelines for Snowflake, BigQuery, and Azure Synapse, balancing ETL and ELT approaches to fit your unique requirements.
Eiji
Founder & Lead Developer at eidoSOFT