Organizations 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. For example, traditional on-premises data warehouses often have limited scalability and processing capabilities compared to modern cloud-based systems. If transformations are performed before loading (ETL), the workload is shifted to a dedicated transformation layer, such as an ETL server, reducing strain on the target system. This is particularly useful when dealing with legacy infrastructure or tightly controlled environments where scaling compute resources isn’t feasible. For instance, a company using an older SQL Server data warehouse might use ETL to pre-process data into a structured format, avoiding performance bottlenecks during query execution.
Another scenario favoring ETL is when strict data governance or compliance requirements demand transformations before storage. Industries like healthcare or finance often need to anonymize, filter, or redact sensitive data before it enters the target system. ETL allows organizations to apply these rules during the transformation phase, ensuring raw sensitive data isn’t persisted. For example, a healthcare provider might use ETL to remove patient identifiers from medical records before loading the data into a reporting database, ensuring HIPAA compliance. ELT, which stores raw data first, could require additional post-load processing to achieve the same compliance, introducing complexity and potential security risks.
Finally, ETL is preferable when organizations prioritize reduced storage costs and optimized data structures upfront. Since ETL transforms data before loading, only curated, relevant datasets are stored in the target system. This avoids the cost of storing large volumes of raw data, which is common in ELT workflows that rely on scalable but expensive cloud storage. For example, a retail company analyzing daily sales might use ETL to aggregate transaction data into summarized tables, discarding redundant or irrelevant details. This approach minimizes storage needs and simplifies downstream analytics, whereas ELT would require storing every raw transaction, increasing costs and complexity for teams that don’t need the granular data.