ELT

A data integration process optimized for modern cloud-based data warehousing. In this approach, raw data is first extracted from multiple sources and directly loaded into a centralized data warehouse. Once inside the warehouse, the transformation step occurs using the platform’s internal processing power, often through SQL-based transformations or built-in data processing engines.

What distinguishes ELT from ETL is the order of operations. ELT defers transformation until after the data has been loaded, allowing for far greater scalability and flexibility. Because modern warehouses, such as Snowflake, BigQuery, and Redshift, can efficiently process large volumes of raw data, ELT leverages their compute capabilities to perform transformations in place. This eliminates the need for an external processing server, reduces latency, and enables analysts to access both raw and processed data for exploratory work.

ELT is best suited for real-time analytics and agile environments where schema changes and data volumes are dynamic. It favors performance and adaptability over rigid structure, providing rapid access to diverse data without the bottleneck of pre-load transformation.