“The new workflow with dbt and Snowflake isn’t a small improvement. It’s a complete redesign of our entire approach to data that will establish a new strategic foundation for analysts at JetBlue to build on.”
Ben Singleton, Director of Data Science & Analytics at JetBlue
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Why dbt + Snowflake?
Silos erode data team effectiveness, and ultimately, data trust. dbt works on top of Snowflake to provide a centralized environment for collaborative data transformation. Now anyone on your data team who knows SQL can collaborate on end-to-end transformation workflows in Snowflake.
[New!] dbt Cloud is now on Snowflake Partner Connect! Get up and running with a free trial instantly through the Snowflake interface.
Hit the ground running
dbt and Snowflake operate in the universal language of data teams: SQL. No new skills are required, and experience is transferable.
Scale up or down with ease
Apply logic in dbt to select the right Snowflake warehouse size for each dbt model, allowing you to control run-time, manage costs, and meet internal data freshness SLAs.
Work in multiple environments
Easily maintain separate production and development environments with fine-grained permissions, mitigating the impact of errors and reducing data downtime.
Practice better data access control
Apply and version control rules to make use of Snowflake’s dynamic data masking from within your dbt workflow, allowing you to control access to PII or other sensitive data.
The Analytics Engineering Workflow
With dbt, data teams work directly within the warehouse to produce trusted datasets for reporting, ML modeling, and operational workflows.
“With Snowflake and dbt, the people who have the business problem now have the tools to go and solve their business problem.”
Ryan Goltz, Principal Data Architect, Fortune 500 Oil & Gas Company