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

dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.

dbt can be used to aggregate pageviews into sessions, calculate ad spend ROI, or report on email campaign performance.

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.

• Install dbt
• Read the documentation.
• Productionize your dbt project with dbt Cloud

• Check out the Introduction to dbt.
• Read the dbt Viewpoint.

• Join the chat on Slack.
• Find community posts on dbt Discourse.

• Want to report a bug or request a feature? Let us know on Slack, or open an issue.
• Want to help us build dbt? Check out the Contributing Getting Started Guide

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