Apache Airflow vs Apache DolphinScheduler

Apache Airflow is a platform for authoring, scheduling, and monitoring workflows written in Python. It is designed for batch workflows with a defined beginning and end that run on a schedule. Its web application lets users manage and schedule workflows, trigger DAGs manually, inspect task status and logs, backfill runs, and rerun failed tasks. A modular architecture uses a message queue to coordinate workers and can orchestrate an arbitrary number of them. Built-in operators connect workflows to Google Cloud Platform, Amazon Web Services, Microsoft Azure, and other services. The community maintains more than 80 separately installable provider packages, which can be upgraded or downgraded independently of Airflow core. Airflow can read connections, variables, and configuration from secret backends, but those integrations do not support writes. JWT authentication is used for its public REST API and internal Execution API. From Airflow 3 onward, sensitive credentials are masked in logs, the interface, and API output. Airflow is free to download and use under the Apache License.

Apache DolphinScheduler is a free, open-source workflow orchestration platform for designing and running data workflows through visual directed-acyclic-graph interfaces or code. Workflows can be built with drag-and-drop tools or defined in Python, YAML, or the Open API. The project lists more than 30 task types, including Shell, SQL, Spark, Flink, Hive, Kubernetes, and HTTP. Scheduling supports cross-project and cross-workflow dependencies, pause and resume operations, and serial or parallel batch backfills. A decentralized multi-master, multi-worker design is described as increasing scheduling capacity with cluster size. Deployment options are standalone, cluster, and Kubernetes. Standalone is for evaluation, recommended for fewer than 20 workflows, and its in-memory H2 database is cleared when the service stops or restarts. Authentication includes password, LDAP, Casdoor SSO, and OAuth2, with administrator-managed resource controls. The security model assumes a trusted network.