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 target system. This approach contrasts with the traditional ETL (Extract, Transform, Load) process by performing transformations after loading, leveraging the processing power of modern cloud-based data platforms. It is commonly used in big data and cloud analytics scenarios to handle large volumes of unstructured or semi-structured data efficiently.
Developers should learn ELT processes when working with cloud data warehouses (like Snowflake, BigQuery, or Redshift) or data lakes, as it allows for faster data ingestion and more flexible, on-demand transformations. It is particularly useful for real-time analytics, handling diverse data sources (e.g., IoT, logs, social media), and scenarios where data volume or velocity makes pre-load transformations impractical. ELT simplifies pipeline maintenance by centralizing transformation logic in the target system, enabling agile data modeling and easier scaling.