ELT, which stands for Extract, Load, Transform, is a data integration process used in modern data warehousing and analytics.
“Extract, Load, Transform is a data integration procedure that moves raw data from a server, like a data warehouse, and then prepares the data for downstream applications.”
In ELT, the initial step is to extract data from various source systems, which can be databases, applications, or even cloud-based services. The extracted data is then loaded into the data warehouse, usually in its raw and unprocessed form. This raw data storage allows for flexibility and scalability, enabling businesses to store vast amounts of data without predefined schemas or transformations.
What is the difference between ELT and ETL?
ELT performs data transformations directly within the data warehouse itself, whereas ETL transforms data on a separate processing server before transferring it to the data warehouse. ELT also allows raw data to be sent directly to the data warehouse, unlike ETL which includes a staging process.
ELT provides several advantages over the traditional ETL process. First, it simplifies data ingestion, as there is no need to design and maintain complex transformation scripts before loading data. Second, ELT allows for real-time or near-real-time data analysis, as the data is loaded as-is and transformations happen within the data warehouse.
Overall, ELT offers a more agile and scalable approach to data integration, allowing businesses to handle massive datasets and derive valuable insights rapidly and cost-effectively.