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dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.

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Understanding dbt

Analysts using dbt can transform their data by simply writing select statements, while dbt handles turning these statements into tables and views in a data warehouse.

These select statements, or "models", form a dbt project. Models frequently build on top of one another – dbt makes it easy to manage relationships between models, and visualize these relationships, as well as assure the quality of your transformations through testing.

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Everyone interacting in the dbt project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the dbt Code of Conduct.

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Credits

Author/Maintainer: dbt Labs

License expression

Apache-2.0
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Requires

Python >=3.10

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