ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are both methods of data integration, but they differ in the order in which the data is processed.
In ETL, data is extracted from various sources, transformed to fit a specific format or structure, and then loaded into a target data store, such as a data warehouse or data lake. The transformation step is done before loading the data into the target data store, and it is typically performed using specialized ETL tools or software. ETl is sometime referred to as schema-on-write.
In ELT, data is extracted from various sources, loaded into a target data store, and then transformed . The transformation step is done after loading the data into the target data store when the data is being read (schema-on-read) allowing the data to be virtualized and the raw data reused for multiple usecases.
The main difference between ETL and ELT is the location where the data is transformed. In ETL, the data is transformed before it is loaded into the target data store, while in ELT, the data is transformed after it is loaded into the target data store.
The main advantage of ELT is that it allows businesses to take advantage of the processing power of modern data warehousing and big data platforms, such as cloud-based data warehouses and data lakes, which can handle large amounts of data and provide fast query performance. This allows businesses to quickly analyze and process large and complex data sets without the need for pre-processing the data and creating a separate data warehouse.
Another advantage of ELT is that it is less complex than ETL as it eliminates the need for specialized ETL tools and software. This reduces the cost and complexity of the data integration process and allows businesses to quickly load and transform data using SQL or other query languages.
Additionally, ELT allows for more flexibility as it allows the data to be loaded into the target data store in its raw form, which allows for more dynamic and ad-hoc analysis.
It’s important to note that ELT is not always the best choice for every situation, depending on the specific needs of the organization, such as the amount of data, the complexity of the data, and the specific use case, ETL might be more suitable.
However, ETL has its own advantages as well. It allows for a more efficient data integration process, as it is possible to perform data validation, data cleansing, data transformation, data deduplication and data mapping before loading the data into the target data store. This can help to reduce the risk of data inconsistencies and errors.
It’s important to note that the choice between ETL and ELT will depend on the specific needs and requirements of the organization, such as the amount of data, the complexity of the data, and the specific use case.
