Today, all major cloud service providers and 3rd party providers include Apache Airflow as a managed service offering in their portfolios. While these cloud based solutions help with the undifferentiated heavy lifting of environment management, some data teams are also looking to operate self-managed Airflow instances to satisfy specific differentiated capabilities. In this session, we would talk about: Why should you might need to run self managed Airflow The available deployment options (with emphasis on Airflow on Kubernetes) How to deploy Airflow on Kubernetes using automation (Helm Charts & Terraform) Developer experience (sync DAGs using automation) Operator experience (Observability) Owned responsibilities and Tradeoffs A thorough understanding would help you understand the end-to-end perspectives of operating a highly available and scalable self managed Airflow environment to meet your ever growing workflow needs.