Astronomer
Managed Apache Airflow service for orchestrating and monitoring data pipelines in the cloud
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Lightweight workflow engine with declarative YAML and Web UI
Dagu is a self-contained workflow scheduler that executes DAGs defined in simple YAML. No coding required, no database dependencies—just install a single binary and start orchestrating pipelines.
Dagu is a lightweight workflow engine designed for teams managing complex job dependencies without the overhead of enterprise platforms. Built for operators dealing with hundreds of cron jobs across legacy systems, Dagu lets you visualize pipelines as DAGs, track execution status, and rerun failed jobs—all through an intuitive Web UI.
Unlike Airflow or similar tools, Dagu requires no programming, no database setup, and no cloud services. Workflows are defined in declarative YAML that wraps existing shell scripts, SSH commands, or Docker containers without modification. Install a single binary, define your steps, and schedule with cron expressions.
Whether you're orchestrating data pipelines, batch processing, or system maintenance tasks, Dagu provides built-in logging, error notifications, and HA mode with distributed task execution. Modularize complex workflows by nesting DAGs, and manage everything from the command line or browser. Ideal for small-to-medium projects where simplicity and maintainability trump feature bloat.
When teams consider Dagu, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Migrating Legacy Cron Jobs
Visualize implicit dependencies between hundreds of cron scripts and manage reruns through a Web UI instead of SSH sessions.
Batch Data Processing Pipelines
Schedule nightly ETL jobs using existing shell scripts and Docker containers without rewriting code in Python.
Remote Server Maintenance
Orchestrate system updates and backups across multiple servers via SSH with centralized logging and error notifications.
Modular Workflow Composition
Break complex pipelines into reusable nested DAGs for easier maintenance and testing.
No. Dagu is self-contained and stores workflow state in local files, eliminating the need for a separate DBMS.
Yes. Dagu wraps shell commands, SSH scripts, and Docker images directly in YAML without requiring code changes.
Dagu is simpler and requires no coding or database setup, making it ideal for small projects. Airflow offers more features for large-scale, programmatic workflows.
Yes. Dagu can run in HA mode and distribute tasks to workers through a built-in queue system.
Install via a single binary (curl script), Docker, Homebrew, or npm. No additional dependencies required.
Project at a glance
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