What is ETL?
ETL is a data integration method that extracts raw data from source systems, transforms it on a secondary processing server, and then loads it into the target database. Raw data is typically transformed outside the data warehouse with the help of a dedicated "staging server," and only the transformed data is loaded into the warehouse.
Extract: The process of pulling data out of a database or source system. In ETL, data is held temporarily in a staging area.
Transform: The process of reshaping information so it fits the structure of the target data system and the other data already held there.
Load: The process of moving the data into a data storage system.
What is ELT?
ELT stands for "Extract, Load, Transform." In this process, the core transformations are carried out directly inside the data warehouse itself, which removes the need for a separate staging stage. ELT relies on cloud-based data warehouse solutions to handle every kind of data — structured, unstructured, semi-structured and even raw. Raw data is transformed inside the warehouse without a staging server, so your warehouse ends up holding both the raw and the transformed data.
What are the differences between ETL and ELT?
ETL and ELT diverge on two key points: where the data is transformed, and how the data warehouse holds it.
In ETL, data is transformed on a separate processing server; in ELT, it is transformed directly inside the data warehouse. ETL never moves raw data into the warehouse; ELT sends the raw data straight into the warehouse.
With ETL, transforming data on a separate server before loading it slows down ingestion. ELT allows for faster ingestion because it never routes data to a second server for restructuring — it can even load and transform data at the same time.
Because ELT preserves the raw data, it builds a rich historical archive for business intelligence. As goals and strategies shift, BI teams can go back to the raw data and build new transformations using the full dataset. ETL, by contrast, does not produce a complete raw dataset that can be queried indefinitely.
These qualities make ELT more flexible, efficient and scalable when you're dealing with high-volume ingestion, a mix of structured and unstructured data, or a wide range of BI needs. ETL, on the other hand, is the better fit for computation-heavy transformations, legacy architectures, or data flows — like cleaning personal data — that need to be manipulated before they ever reach the target system.