I find that comparison interesting, because I don't think of Airflow as particularly data-oriented in terms of its features and core functionality. I tend to think of Airflow as "Cron + Make", with any "data-oriented" features being nice to have, but not essential.

I'm substantially less familiar with Dagster and Prefect so can't comment as much on those.

Maybe the most data-oriented thing about Airflow is its concept of a data interval, where each DAG run is associated with some "logical date" and an interval of time that starts from the logical date (inclusive) and ends at the next logical date in the schedule (exclusive). The idea is that if you have a daily task that runs at 1 AM, then the task is expected to operate on data starting from "yesterday at 1 AM" until "today at 1 AM". But it's entirely up to the user/developer what you actually do with those logical date ranges, and you're free to ignore them entirely if you don't need them.