ELT
ELT (Extract, Load, Transform) is a data integration methodology where raw data is first extracted from source systems, loaded directly into a target data warehouse or data lake, and then transformed within that destination system. This approach contrasts with the traditional ETL (Extract, Transform, Load) process by deferring transformation until after loading, leveraging the processing power of modern cloud-based data platforms. It enables faster data ingestion and more flexible, on-demand transformations tailored to specific analytical needs.
Developers should learn ELT when working with large-scale, cloud-based data architectures, such as data lakes or modern data warehouses like Snowflake or BigQuery, where storage is cheap and compute can be scaled dynamically. It is particularly useful for real-time analytics, handling unstructured or semi-structured data, and scenarios requiring rapid data availability, as it minimizes latency during the initial load phase. ELT also supports agile data modeling, allowing transformations to be iteratively refined without disrupting data ingestion pipelines.