<p>Does the Executor automatically send tasks to pods, or do I need the operator to do that? </p>

<p>Each operator has the "executor_config" param so I'm not sure when to use either.</p>

AlanPear
<p>They serve different purposes. </p>

<p>Briefly:</p>

<p><strong>KubernetesExecutor</strong>: You need to specify one of the supported executors when you set up Airflow. The executor controls how all tasks get run. In the case of the KubernetesExecutor, Airflow creates a pod in a kubernetes cluster within which the task gets run, and deletes the pod when the task is finished.</p>

<p>Basically, you would use this instead of something like Celery.</p>

<p><strong>KubernetesPodOperator</strong>: This allows you to, essentially, run a container as a task, and that container will be run inside a pod on a kubernetes cluster. </p>

<p>You'd use this if you if you have containerized workloads that you need to schedule in Airflow, or if you have non-python code you want to execute as Airflow tasks.</p>

<p>Hope that is helpful.</p>
