Apache DolphinScheduler vs Dagster

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.

Dagster is a data-aware orchestrator for building, running and monitoring data pipelines and assets. It models assets and dependencies to show lineage across a data platform, while supporting partitions, incremental runs, declarative workflows and fault-tolerant orchestration. An integrated catalog combines data discovery, lineage, workflows and operational metadata. Quality features include code-embedded validation, automated testing, freshness checks and observability tools. The integration directory lists Airbyte, Anthropic, Apache Airflow, AWS services and Azure Data Lake Storage. Dagster+ hybrid deployment keeps compute in a customer’s infrastructure while Dagster manages the control plane. The core framework is open source on GitHub. Platforms listed are API, Linux, self-hosted and web; deployment is hybrid. A 30-day trial is listed. Solo costs $10 a month plus usage charges, and Starter costs $100 a month plus usage charges. Dagster’s website says it is now part of Prefect.