DAGForge provides everything you need to build, deploy, and manage Airflow DAGs efficiently.
Visual DAG Builder
Professional IDE-like workflow canvas for building Apache Airflow DAGs with comprehensive visual editing capabilities.
Visual Editing Features
Import & Export Capabilities
Python Import
- Import existing Python DAG files
- Automatic parsing and validation
- Convert to visual workflow
- Preserve custom functions and imports
DAG Export
- Export as Python (.py) files
- Production-ready code generation
- Automatic filename based on DAG ID
- One-click download
Templates & Collaboration
Template Management
- Save DAGs as reusable templates
- Share templates across teams
- Duplicate existing DAGs
- Copy from existing workflows
GitHub Integration
- Sync DAGs to GitHub repositories
- Automatic version control
- One-click deployment to Airflow
- Team collaboration workflows
Advanced Features
Keyboard Shortcuts
Ctrl+A Select AllCtrl+C/V Copy/PasteCtrl+Z/Y Undo/RedoDel DeleteCtrl+F Fit to ScreenCanvas Operations
Validation & Help
Production-Ready Code Generation
DAGForge generates production-ready Python code that follows industry best practices, ensuring your DAGs are secure, maintainable, and performant from day one.
Python Best Practices
- PEP 8 Compliance: All generated code follows Python's official style guide
- Type Hints: Comprehensive type annotations for better code clarity
- Docstrings: Detailed documentation for all functions and classes
- Error Handling: Comprehensive try-catch blocks and exception management
Airflow Best Practices
- Resource Management: Optimal pool and queue configurations
- Security: Secure connection handling and credential management
- Monitoring: Built-in logging, alerting, and SLA configurations
- Scalability: Proper parallelism and concurrency settings
Provider Management
Access 74+ providers with 461+ components for comprehensive data integration.
Databases
PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch
Cloud Platforms
AWS, GCP, Azure, Snowflake, Databricks
Communication
Slack, Discord, Telegram, Email, SMS
Development
GitHub, Jenkins, Docker, SSH, SFTP
Template Library
Start with pre-built templates for common workflows.
Data Pipeline
ETL workflows and data processing
Workflow Control
Conditional logic and branching
Monitoring
Health checks and alerting
System Automation
Infrastructure and deployment
Orchestration
Complex workflow management
Data Quality
Validation and compliance
GitHub Integration
Seamlessly sync and deploy DAGs with your existing Git workflow.
What You Get:
Platform Architecture
Organizations
Top-level containers for teams and companies with billing and user management.
Workspaces
Isolated environments for different projects with separate configurations.
Projects
Logical groupings of related DAGs with shared resources and settings.
DAGs
Individual data pipelines and workflows with version control and monitoring.
Dashboard & Analytics
Analytics Dashboard
Real-time statistics, performance metrics, and usage analytics
Smart Search
Search across DAGs, projects, and workspaces with filters
Status Monitoring
Track production readiness, sync status, and validation errors
Activity Tracking
Monitor user actions, DAG executions, and system events
Performance Metrics
Track execution times, success rates, and resource usage
Alerts & Notifications
Get notified of failures, performance issues, and system events
Enterprise Features
Multi-tenant Security
Secure, isolated environments with role-based access control and data encryption.
Team Management
Invite team members, assign roles, and manage permissions across workspaces.
Billing & Usage
Track usage, manage subscriptions, and monitor costs with detailed analytics.
Integrations
SSO, Slack notifications, webhook integrations, and third-party connections.
Audit Logs
Comprehensive audit trails for compliance and security monitoring.
Real-time Validation
Catch errors before deployment with instant feedback and suggestions.
Supported Workflows
Complete DAG Builder Workflow
Understanding the logical flow of building DAGs in DAGForge - from creation to deployment.
Step-by-Step DAG Creation Process
Create or Import
Start with a blank canvas, import existing Python DAGs, or use pre-built templates
Design Workflow
Use AI Assistant or drag-and-drop interface to build your data pipeline
Configure & Validate
Set DAG parameters, configure tasks, and get real-time validation feedback
Save & Version
Save your DAG with automatic versioning and optional template creation
Deploy & Monitor
Export Python code, sync to GitHub, or deploy directly to Airflow
Key DAG Builder Capabilities
Multi-tab Interface
Settings, Workflow, Code, and Versions tabs
Auto-save Protection
Prevents data loss with unsaved changes detection
Version Control
Track changes and restore previous versions
Error Recovery
Navigate to errors and get helpful suggestions
Custom Functions
Add Python functions and custom imports
Production Ready
Generate deployment-ready Python code