#Managed Apache Airflow on AWS

• You need to specify a bucket that must start with airlow in the name.
• Specify different paths for Dags, Plugins and Requirements.

• Create a new VPC and select the type of deployment (public or private).
• Select the environment class, to start I suggest mw1.small.
• For monitoring in production environment it is better to enable form INFO logs for tasks, webserver, scheduler, worker and DAGs.
• Create a custom role and give the role permissions over the S3 bucket. (Remember give permissions to the rone in the Bucket policy).

• Store all the DAGs in the dags folder.
• The DAGs should finish with .py extension.

• Take into account that the latest version is 1.10.12 and you will have all the default packages that come with the version.
• In the requirements file you need to specify additional packages in this way: apache-airflow[slack, package2, package3].
• If you need to use custom operators you need to call them from the dag file: from airflow.contrib.operators.slack_webhook_operator import SlackWebhookOperator