ETL
ETL (Extract, Transform, Load) is a traditional data integration process where data is first extracted from source systems, then transformed (cleaned, aggregated, or restructured) in a staging area, and finally loaded into a target data warehouse or database. It is a batch-oriented approach commonly used for structured data to support business intelligence and reporting. The transformation step occurs before loading, ensuring data quality and consistency in the destination.
Developers should learn ETL when working with legacy systems, structured data warehouses, or scenarios requiring strict data governance and pre-load validation, such as financial reporting or regulatory compliance. It is ideal for batch processing where data freshness is less critical than accuracy, and transformations are complex and resource-intensive. Use cases include migrating data from on-premises databases to cloud data warehouses like Snowflake or Amazon Redshift.