I am a data engineer and work with airflow regularly.
When redeploying dags with a new start date the best practice is as shown in the here:
Don’t change start_date + interval: When a DAG has been run, the scheduler database contains instances of the run of that DAG. If you change the start_date or the interval and redeploy it, the scheduler may get confused because the intervals are different or the start_date is way back. The best way to deal with this is to change the version of the DAG as soon as you change the start_date or interval, i.e. my_dag_v1 and my_dag_v1. This way, historical information is also kept about the old version.
However after deleting all previous DAG and task runs I tried to redeploy a dag with a new start date. It worked as expected (with the new start date) for a day, then started to work with the old again
What are the reasons for this? In depth if you can.