[Grimm1]

Airflow allows for more complex transformations of data that SQL may not be suited for. DBT is largely stuck utilizing the SQL capabilities of the warehouse it sits on, so for instance, with Redshift you have a really bad time working with JSON based data with DBT, Airflow can solve this problem. That's one example, but last I was working with it we found DBT was great for analytical modeling type transformations but from getting whatever munged up data into a useable format in the first place Airflow was king.

We also trained our analysts to write the more analytical DBT transformations which was nice, shifted that work onto them.

Don't get me wrong though, you can get really far with just DBT + Fivetran, in fact, it removes like 80% of the really tedious, but trivial ETL work. Airflow is just there for the last 20%

(Plus you can then utilize airflow as a general job scheduler)