What is ETL and how does it differ from ELT? When would you choose one approach over the other for a BI project?

Question

Accepted Answer

ETL and ELT are both approaches to moving data from source systems to analytical environments, but they differ in where transformation occurs, which has significant implications for architecture and tooling.

ETL stands for Extract, Transform, Load. Data is extracted from source systems, transformed in a staging area or dedicated processing server, and then loaded into the target data warehouse. The transformation happens outside the warehouse using tools like Informatica, Talend, or SSIS. The data warehouse receives clean, transformed data ready for analysis.

ELT stands for Extract, Load, Transform. Data is extracted from source systems and loaded directly into the target platform with minimal transformation. The heavy transformation work then happens inside the target system using its native processing power. Modern cloud warehouses like Snowflake, BigQuery, and Redshift have powerful compute engines that handle transformations efficiently. Tools like dbt have popularized SQL-based transformations within the warehouse.

Choose ETL when working with on-premises data warehouses that have limited compute capacity. The external transformation server handles the processing load, protecting the warehouse for query workloads. ETL is also appropriate when sensitive data needs to be cleansed or masked before it enters the warehouse for compliance reasons.

Choose ELT when using cloud data warehouses with elastic compute capacity. These platforms can scale processing power on demand, making in-warehouse transformation efficient and cost-effective. ELT simplifies the architecture by eliminating the need for a separate transformation server. It also preserves raw data in the warehouse, enabling analysts to build new transformations without re-extracting from sources.

The trend in modern BI strongly favors ELT. Cloud warehouse pricing models, the separation of compute from storage, and tools like dbt have made ELT the default approach for new BI projects. Raw data is loaded quickly, and transformations are version-controlled SQL managed by the BI or analytics engineering team.

For BI analysts, understanding the pipeline approach matters because it affects data freshness, troubleshooting, and the ability to modify business logic. In an ELT environment, you may have more direct access to transformation logic and the ability to add new derived tables without involving a dedicated ETL team.