Hi
we are currently running dbt on Airflow (actually Astronomer managed airflow) but are performing a proof of concept (POC) study to determine if we can migrate to dbt cloud. I would like to know if there is a list of dbt features that are not currently supported on dbt cloud? For example it is only by reading a thread on this forum that I discovered that env_var is not supported on dbt cloud, which will entail quite a significant rework of our models.
Many thanks
As an aside I also think it would be great if there were a dbt cloud Category on this forum
Sorry for the late reply here — I really thought about this and environment variables were the only thing that sprung to mind as a “missing” feature of dbt Cloud.
There will be differences in deploying a project via dbt Cloud versus Airflow, but they aren’t really “features” per se. Things like:
Jobs in dbt Cloud are configured through a UI, whereas tasks in Airflow are defined in your code (as far as I know)
You can chain together complex workflows in Airflow, whereas dbt Cloud is purpose-built for running dbt jobs only.
In exchange though, you get a ton of things that make running dbt jobs much nicer, like:
Access to the IDE, which might be a nicer development experience for team members
Automatically generate documentation for your project, in a way that members of your account can access securely (with lots of read only accounts)
Run your project automatically on pull requests
By the way, I’m going to unlist this topic — we’re trying to use Discourse for more long-form discussions, and “How to” posts, particularly those around dbt Core. Check out more details here.
Happy to continue the discussion on dbt Slack in the #dbt-cloud channel