Transforming data. Transforming teams. dbt™ helps data teams work like software engineers—to ship trusted data, faster.
The analytics engineering workflow.
With dbt, data teams work directly within the warehouse to produce trusted datasets for reporting, ML modeling, and operational workflows.
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.
Write modular data transformations in .sql or .py files – dbt handles the chore of dependency management.LEARN MORE
The new standard for data transformation
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.LEARN MORE
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.LEARN MORE
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.LEARN MORE
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.LEARN MORE
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