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.


The new standard for data transformation

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.


dbt Resources

Snowflake Data Cloud World Tour Auckland 2023

Join us and your fellow data, technology and business leaders to learn about the latest capabilities of the Data Cloud and hear directly from our customers about their most exciting use cases.


Getting Started with Cloud-based Data Analytics

Organisations are leveraging cloud-based data analytics solutions for the flexibility and scalability they provide, but security is still a significant hurdle to cloud migration. Explore Challenges of user authentication and authorisation.


Is Your Company Culture Prepared for Cloud Data Analytics?

Organisations are migrating data to the cloud to reap the benefits, but it’s essential to consider how your people and systems will be impacted by a new data pipeline and what tools and training will be involved if you move to a more automated analytics platform.


Why Data Collaboration is a Necessity for Strategic Decision-Making

Without company-wide visibility into data, it’s difficult or even impossible for leaders to make data-driven decisions outside of departmental bubbles. This challenge is especially transparent in verticals.


New and Exciting Partnerships

Leading technologies, Qlik, Snowflake and DataRobot provide scalable and automated data platforms as well as automated advanced analytics/machine learning. Ensuring there is excellent integration to facilitate the use of scalable real-time analytics.


Innovation & Analytics Strategy – Webinar (Donald Farmer)

Donald Farmer discusses the Principle of TreeHive Strategy. Explore how to define and measure innovation in a very practical way, enabling teams to quickly and effectively become more innovative.