Extract, Load, Transform (ELT) is a modern data integration pattern where raw data is extracted from sources, loaded directly into a cloud data warehouse or lakehouse, and then transformed in-place using the warehouse's native compute power. Unlike traditional ETL, which transforms data before loading, ELT shifts transformation downstream, leveraging scalable cloud infrastructure for processing. This approach simplifies pipelines, preserves raw data for flexibility, and enables analysts and data engineers to iteratively refine transformations using SQL-based tools like dbt. ELT has become the foundation of the modern data stack, powering analytics, machine learning, and operational systems. The key mental model: storage is cheap, compute is elastic—load first, transform later.