DAGify is a highly extensible, template driven, enterprise scheduler migration accelerator that helps organizations speed up their migration to Apache Airflow. While DAGify does not claim to migrate 100% of existing scheduler functionality it aims to heavily reduce the manual effort it takes for developers to convert their enterprise scheduler formats into Python Native Airflow DAGs. DAGify is an open source tool under Apache 2.0 license and available on Github (https://github.com/GoogleCloudPlatform/dagify). In this session we will introduce DAGify, its use cases and demo its functionality by converting Control-M XML files to Airflow DAGs. Additionally we will highlight DAGify’s “no-code” extensibility by creating custom conversion templates to map Control-M functionality to Airflow operators.
