diff --git a/docs/modules/airflow/examples/example-airflow-kubernetes-executor-s3-logging.yaml b/docs/modules/airflow/examples/example-airflow-kubernetes-executor-s3-logging.yaml
new file mode 100644
index 00000000..0c88c900
--- /dev/null
+++ b/docs/modules/airflow/examples/example-airflow-kubernetes-executor-s3-logging.yaml
@@ -0,0 +1,24 @@
+apiVersion: airflow.stackable.tech/v1alpha1
+kind: AirflowCluster
+metadata:
+  name: airflow
+spec:
+  image:
+    productVersion: 2.9.3
+  clusterConfig: {}
+  webservers:
+    envOverrides: &s3-logging-env-overrides
+      AIRFLOW_LOGGING_REMOTE_LOGGING: "True"
+      AIRFLOW_LOGGING_REMOTE_BASE_LOG_FOLDER: s3://<bucket-name>/airflow-task-logs/
+      # The name / connection ID created in the Airflow Web UI
+      AIRFLOW_LOGGING_REMOTE_LOG_CONN_ID: minio
+    roleGroups:
+      default:
+        replicas: 1
+  schedulers:
+    envOverrides: *s3-logging-env-overrides
+    roleGroups:
+      default:
+        replicas: 1
+  kubernetesExecutors:
+    envOverrides: *s3-logging-env-overrides
diff --git a/docs/modules/airflow/images/airflow_dag_s3_logs.png b/docs/modules/airflow/images/airflow_dag_s3_logs.png
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diff --git a/docs/modules/airflow/images/airflow_edit_s3_connection.png b/docs/modules/airflow/images/airflow_edit_s3_connection.png
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diff --git a/docs/modules/airflow/pages/usage-guide/using-kubernetes-executors.adoc b/docs/modules/airflow/pages/usage-guide/using-kubernetes-executors.adoc
index 1ba069a5..c5771752 100644
--- a/docs/modules/airflow/pages/usage-guide/using-kubernetes-executors.adoc
+++ b/docs/modules/airflow/pages/usage-guide/using-kubernetes-executors.adoc
@@ -3,6 +3,8 @@
 
 Instead of using the Celery workers you can let Airflow run the tasks using Kubernetes executors, where Pods are created dynamically as needed without jobs being routed through a Redis queue to the workers.
 
+== Kubernetes Executor configuration
+
 To achieve this, swap `spec.celeryExecutors` with `spec.kubernetesExecutors`.
 E.g. you would change the following example
 
@@ -28,3 +30,40 @@ spec:
       resources:
         # ...
 ----
+
+== Logging
+
+Kubernetes Executors and their respective Pods only live as long as the task they are executing.
+Afterwards the Pod is immediately terminated and e.g. console output or logs are gone.
+
+In order to persist task logs, Airflow can be configured to store its https://airflow.apache.org/docs/apache-airflow-providers-cncf-kubernetes/stable/kubernetes_executor.html#managing-dags-and-logs[executor logs on disk (PV)] or as described in the following section on S3.
+
+=== Airflow Web UI
+
+In the Airflow Web UI, click on `Admin` -> `Connections` -> `Add a new record` (the plus).
+Then enter your MinIO host and credentials as shown.
+
+image::airflow_edit_s3_connection.png[Airflow connection menu]
+
+The name or connection ID is `minio`, the type is `Amazon Web Services`, the `AWS Access Key ID` and `AWS Secret Access Key` are filled with the S3 credentials.
+The `Extra` field contains the endpoint URL like:
+
+[source,json]
+----
+{
+  "endpoint_url": "http://minio.default.svc.cluster.local:9000"
+}
+----
+
+=== Executor configuration
+
+In order to configure the S3 logging, you need to add the following environment variables to the Airflow cluster definition:
+
+[source,yaml]
+----
+include::example$example-airflow-kubernetes-executor-s3-logging.yaml[]
+----
+
+Now you should be able to fetch and inspect logs in the Airflow Web UI from S3 for each DAG run.
+
+image::airflow_dag_s3_logs.png[Airflow DAG S3 logs]