Astronomer
Managed Apache Airflow service for orchestrating and monitoring data pipelines in the cloud
Dagu
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.
Overview
Simplify Legacy Workflow Management
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.
Zero-Dependency Architecture
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.
Built for Operations Teams
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.
Highlights
Single binary installation with zero external dependencies or databases
Declarative YAML workflows wrapping shell scripts, SSH commands, and Docker
Built-in Web UI for visualizing DAGs, logs, and execution control
HA mode with distributed task queuing and cron-based scheduling
Pros
- No coding required—wrap existing scripts and commands in YAML
- Self-contained deployment with no DBMS or cloud service dependencies
- Intuitive Web UI for monitoring, logs, and one-click job reruns
- Supports remote SSH execution and Docker containers out of the box
Considerations
- Designed for small-to-medium projects; may lack enterprise-scale features
- Limited ecosystem compared to mature platforms like Airflow
- YAML-only workflow definitions may not suit complex programmatic logic
- Newer project with smaller community and fewer integrations
Managed products teams compare with
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.
Fit guide
Great for
- Operations teams managing legacy cron jobs and shell scripts
- Small-to-medium data pipelines requiring simple orchestration
- Environments where minimal dependencies and easy deployment are critical
- Teams needing quick visibility into job dependencies and execution logs
Not ideal when
- Large-scale enterprise workflows requiring advanced orchestration features
- Projects needing deep integration with cloud-native data platforms
- Teams requiring programmatic DAG generation in Python or other languages
- Use cases demanding extensive plugin ecosystems or third-party connectors
How teams use it
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.
Tech snapshot
Go76%
TypeScript23%
JavaScript1%
Makefile1%
CSS1%
Shell1%
Frequently asked questions
Does Dagu require a database?
No. Dagu is self-contained and stores workflow state in local files, eliminating the need for a separate DBMS.
Can I use existing scripts without modification?
Yes. Dagu wraps shell commands, SSH scripts, and Docker images directly in YAML without requiring code changes.
How does Dagu compare to Airflow?
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.
Does Dagu support distributed execution?
Yes. Dagu can run in HA mode and distribute tasks to workers through a built-in queue system.
What installation methods are available?
Install via a single binary (curl script), Docker, Homebrew, or npm. No additional dependencies required.
Project at a glance
Active- Stars
- 2,997
- Watchers
- 2,997
- Forks
- 222
LicenseGPL-3.0
Repo age3 years old
Last commit3 hours ago
Primary languageGo
Last synced 3 hours ago