ETL vs ELT: When to Choose Which

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) sound similar, but the order of operations changes everything. Here's when to use each—and why modern data platforms favor ELT.

ETL: Transform Before Load

In ETL, you transform data in a staging layer (e.g., ADF, SSIS) before loading into the target. The target receives only cleansed, structured data. This made sense when target systems (e.g., data warehouses) had limited compute.

ELT: Load First, Transform in Place

In ELT, you load raw data into the target (e.g., a lakehouse) and transform there using SQL or Spark. The target's compute does the heavy lifting. This leverages cloud-scale engines like Databricks and Snowflake.

When to Choose

Choose ETL when:

• Target system has limited compute or storage
• Regulatory requirements demand transformation before storage
• You need to integrate with legacy systems that expect specific formats

Choose ELT when:

• You have a scalable data lake or lakehouse
• Use cases evolve—raw data preserves flexibility
• You want to minimize pipeline complexity and leverage SQL/Spark

Conclusion

For most modern cloud data platforms, ELT is the default. Load raw data, transform with SQL or Spark, and iterate. Keep ETL in mind for edge cases where pre-load transformation is required.