New in dbt: allow Snowflake Python models to access the internet

With dbt 1.8, dbt released support for Snowflake’s external access integrations further enabling the use of dbt + AI to enrich your data. This allows querying of external APIs within dbt Python models, a functionality that was required for dbt Cloud customer, EQT AB. Learn about why they needed it and how they helped build the feature and get it shipped!

Overview​

Over the course of my three years running the Partner Engineering team at dbt Labs, the most common question I've been asked is, How do we integrate with dbt? Because those conversations often start out at the same place, I decided to create this guide so I’m no longer the blocker to fundamental information. This also allows us to skip the intro and get to the fun conversations so much faster, like what a joint solution for our customers would look like.

This guide doesn't include how to integrate with dbt Core. If you’re interested in creating a dbt adapter, please check out the adapter development guide instead.

Instead, we're going to focus on integrating with dbt Cloud. Integrating with dbt Cloud is a key requirement to become a dbt Labs technology partner, opening the door to a variety of collaborative commercial opportunities.

Here I'll cover how to get started, potential use cases you want to solve for, and points of integrations to do so.