ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two different approaches to integrating data from different sources into a single data warehouse.
In the traditional ETL approach, data is first extracted from the source systems, then transformed to match the destination schema, and finally loaded into the data warehouse. This approach is useful when the data needs to be transformed significantly before being loaded into the warehouse. ETL tools typically have built-in data transformation capabilities.
In contrast, ELT reverses the order of transformation and loading. In ELT, data is first extracted from the source systems and loaded directly into the data warehouse. Then, transformation is applied on the data within the data warehouse using SQL queries or other tools. This approach is useful when the destination schema is more flexible and can handle a wide variety of source data formats. ELT is also useful when the data volumes are large, as the data can be transformed within the more powerful data warehouse infrastructure.
Both ETL and ELT have their advantages and disadvantages, and the choice between the two depends on the specific needs of the organization. In general, ETL is better suited for situations where the data needs significant transformation before loading into the data warehouse, while ELT is better suited for situations where the data volumes are large and the destination schema is more flexible.