dbt

How dbt works

Version Control and CI/CD

Deploy safely using dev environments. Git-enabled version control enables collaboration and a return to previous states.

Test and Document

Test every model prior to production, and share dynamically generated documentation with all data stakeholders.

Develop

Write modular data transformations in .sql or .py files – dbt handles the chore of dependency management.

Develop faster

Replace boilerplate DDL/DML with simple SQL SELECT statements that infer dependencies, build tables and views, and run models in order. Develop code that writes itself with macros, ref statements, and auto-complete commands in the Cloud IDE. Make use of Python packages to speed up complex analysis.

Work from the same assumptions

dbt’s pre-packaged and custom testing helps developers create a “paper-trail” of validated assumptions for data collaborators. Auto-generated dependency graphs and dynamic data dictionaries promote trust and transparency for data consumers.

Deploy with confidence

Build observability into transformation workflows with in-app scheduling, logging, and alerting. Protection policies on branches ensure data moves through governed processes including dev, stage, and prod environments generated by every CI run.

Eliminate silos

Now your data science team can build models that connect with those built by the analytics team, each using the language they prefer. dbt supports modeling in SQL or Python, enabling a shared workspace for everyone that works on analytic code.