In what scenarios might an organization choose ETL over ELT?

An organization might choose ETL (Extract, Transform, Load) over ELT (Extract, Load, Transform) when the target system lacks the computational power or flexibility to handle complex transformations efficiently. ETL processes data through a dedicated transformation layer (like a middleware server or ETL tool) before loading it into the destination, which is useful when the destination is a legacy data warehouse or a system with limited processing resources. For example, older on-premises databases might struggle with heavy SQL-based transformations, making ETL a better fit to offload that work to a separate engine. This approach ensures the target system isn’t overwhelmed and maintains performance for querying and reporting.

Another scenario favoring ETL is when strict data governance or compliance requires transformations to occur before storage. For instance, industries like healthcare or finance often need to anonymize or mask sensitive data (e.g., patient IDs or credit card numbers) before it enters the target system. With ETL, transformations like encryption, aggregation, or filtering can be applied upfront, ensuring only compliant data is stored. This reduces the risk of exposing raw sensitive data in the target system, which might lack fine-grained access controls. A practical example is a hospital using ETL to strip personally identifiable information (PII) from patient records before loading them into a reporting database, adhering to HIPAA regulations.

Finally, ETL is preferable when integrating data from multiple sources into a unified format for a specific downstream use case. For example, a retail company combining sales data from legacy POS systems, e-commerce platforms, and third-party APIs might use ETL to standardize date formats, currency conversions, and product categorizations before loading into a data warehouse. This ensures consistency and reduces complexity in the target system, as transformations are handled once during the pipeline. ELT, by contrast, might require repeated transformations in the warehouse, increasing compute costs and complexity. ETL’s centralized transformation layer simplifies maintenance in such scenarios.