ELT is a modern variation of ETL where data is extracted from source systems, loaded directly into a data warehouse, and then transformed inside the warehouse. The main difference lies in where transformations occur.

In ELT:

• Data is extracted from sources

• Loaded in raw form into the warehouse

• Transformed using SQL and warehouse compute

ELT became popular with the rise of cloud data warehouses like Snowflake, BigQuery, and Redshift, which can scale compute on demand.

Benefits of ELT include:

• Faster data ingestion

• Greater flexibility

• Easier schema evolution

• Transparent transformation logic

Tools like dbt are commonly used to manage ELT transformations.

ELT enables analytics teams to iterate faster and apply transformations closer to where data is consumed. It is now the dominant approach in modern BI stacks.