ELT Process
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. This approach contrasts with the traditional ETL (Extract, Transform, Load) process by performing transformations after loading, leveraging the processing power of modern cloud data platforms. It is commonly used in big data and cloud analytics environments to handle large volumes of unstructured or semi-structured data efficiently.
Developers should learn ELT when working with cloud-based data warehouses like Snowflake, BigQuery, or Redshift, as it allows for scalable processing of massive datasets without upfront transformation bottlenecks. It is ideal for real-time analytics, data lake architectures, and scenarios where data schemas are flexible or unknown at ingestion time. Use cases include log analysis, IoT data streams, and modern business intelligence platforms that require agile data modeling.