These activities are usually managed by cron and more often by advanced scheduler tools (depending on the vendor), so it's quite a core part of any architecture that needs to e.g. load/reload/refresh data periodically.
If the requirement is simply to connect notebooks to a data lake, then the only scheduling required is to load the data lake, and something like Airflow may be overkill for this, depending on what/how the data is processed and loaded.
I wonder what your issue is/was? Notebooks are supported by means of a Papermill operator (equivalent to how Netflix operationalizes notebooks) or PythonOperator/BashOperator which would just wrap around your notebook.
However to parralelize tasks Airflow needs to know a bit more hence you might have found it required to break up your notebook into individual tasks that combine into a DAG. Is that what you meant?
Jupyter doesn't do scheduling and integrates pretty well with Airflow.
Prefect was started by an Airflow maintainer and friend of mine who also contributed the dask executor to Airflow. Hi @Jeremiah!