The shift to cloud warehouses has changed how we design data pipelines. Here’s why ELT is now the default architecture.

For years, ETL (Extract → Transform → Load) was the standard way to build data pipelines. Data was extracted from source systems, transformed on intermediate servers, and then loaded into a database or warehouse.

But with the rise of cloud data warehouses like BigQuery, Snowflake, and Redshift, the model evolved. These platforms are extremely fast and cost-efficient for transformations, so modern pipelines follow ELT: Extract → Load → Transform.

This approach simplifies architecture, reduces compute infrastructure, and keeps raw data available for future use cases. Combined with tools like dbt, engineers can version, test, and document transformations directly inside the warehouse.