Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) are two common techniques used in data analytics. They prepare and move data from source systems to a target data warehouse or data storage for analysis.
The ETL process is typically used in a data warehouse architecture. The data is first extracted from source systems and then goes through the transformation process to cleanse the data to prepare for analysis. Once the data has been transformed, it is loaded into a target data warehouse or a data mart. This load process typically maps the transformed data into tables and structures in the target systems.
The ELT process, typically used in a modern data stack, starts with data extraction from source systems. This technique works well in a modern data stack architecture. Data is extracted and loaded into the target storage as-is, with little transformation. The transformation step is performed directly on the target systems using integrated processing capabilities without cleansing the data first. This approach takes advantage of the modern data stack’s compute power to transform the data as needed during the analysis phase.
ELT compared to ETL shows the differences between the ETL and ELT processes.
Both ETL and ELT methods can be used simultaneously, and each has its own benefits. The choice between them depends on factors such as data volume, data transformation complexities, and capabilities of the data warehouse or storage system being used for analysis.