ETL stands for Extract, Transform, Load. It is a data integration process used to move data from source systems into a data warehouse or analytics system in a structured and usable form. ETL has been a foundational concept in BI and analytics for decades.

The ETL process works in three stages:

• Extract: Data is pulled from source systems such as databases, SaaS applications, APIs, or flat files.

• Transform: The extracted data is cleaned, standardized, enriched, and reshaped according to business rules.

• Load: The transformed data is loaded into a target system such as a data warehouse or data mart.

ETL is commonly used for reporting, financial analysis, compliance, and historical trend analysis. It ensures data consistency by applying transformations before data reaches the analytics layer.

Traditional ETL systems often perform transformations in a separate processing layer before loading data into the warehouse. This approach was necessary when warehouses had limited compute power.

ETL provides strong control over data quality, schema enforcement, and validation. However, it can be slower to adapt to schema changes and may require more upfront design.

Despite newer approaches, ETL remains widely used in regulated industries and legacy enterprise systems.