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Overview

dlt supports both Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) patterns.

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Transform your data with @dlt.hub.transformation — eagerly on your compute or lazily as SQL pushed down to the warehouse, with incremental processing out of the box. See the dltHub transformations overview or follow the full guide.

In ETL, the data is transformed before being loaded into the destination. This is useful for light processing such as adding columns, removing sensitive data, or type casting. dlt offers built-in utilities like add_map() and custom processors via @dlt.transformer

In ELT, the data is loaded as-is in the destination. This raw data is transformed directly on the destination where more powerful compute is available (e.g., data warehouse, data lake). dlt supports this via several patterns.

The two approaches can be used together in a single project.

This demo works on codespaces. Codespaces is a development environment available for free to anyone with a Github account. You'll be asked to fork the demo repository and from there the README guides you with further steps.
The demo uses the Continue VSCode extension.

Off to codespaces!

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