In what scenario is Extract, Load, Transform (ELT) typically used?

ELT, or Extract, Load, Transform, is primarily used in scenarios involving data lakes. This approach is particularly advantageous because data lakes store a vast amount of raw data in its native format, allowing for flexibility in handling different data types and structures. In an ELT process, data is first extracted from a source and then loaded into the data lake. The transformation of data occurs afterward, allowing users to apply various transformations based on the specific needs of their analysis or reporting.

This is distinct from environments like traditional data warehouses, where ETL (Extract, Transform, Load) is more common. In those cases, data is transformed before being loaded into the warehouse, making it more structured and prepared for immediate analytics upon loading.

Utilizing ELT in data lakes supports big data analytics and machine learning projects, where the volume and variety of data can be vast and complex, providing significant advantages in terms of scalability and the ability to query data in its raw state.