Follow these simple steps to create your first Apache Airflow DAG with DAGForge. No prior Airflow experience required!
Understanding Airflow & DAGs
What is Apache Airflow?
Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. Think of it as a conductor for your data orchestra.
DAGs (Directed Acyclic Graphs) are the workflows that define how your data moves and transforms through different tasks.
Common Use Cases:
- ETL pipelines (Extract, Transform, Load)
- Machine Learning workflows
- Data quality monitoring
- Business intelligence reporting
Why Choose DAGForge?
Traditional Airflow Development:
- Weeks of manual Python coding
- Complex operator configuration
- Steep learning curve
- Time-consuming debugging
With DAGForge:
- Minutes of visual design
- AI-powered code generation
- Built-in best practices
- Production-ready from day one
Built-in Best Practices
DAGForge automatically follows industry best practices, so you don't have to worry about code quality, security, or performance optimization.
Step 1: Create Your Account
Sign up with your email or GitHub account. No credit card required.
Supported Environments: AWS, GCP, Azure, or Local
Step 2: Choose Your Method
AI Assistant (Recommended)
Describe what you want to do in plain English:
"Extract data from PostgreSQL, transform it, and load to BigQuery daily at 2 AM"
Visual Builder
Drag and drop tasks to build your workflow visually.
Step 3: Build Your First DAG
Try one of these simple examples:
Data Transfer
AI Prompt:
"Copy data from MySQL to PostgreSQL every hour"
File Processing
AI Prompt:
"Process CSV files from S3 and load to BigQuery"
Report Generation
AI Prompt:
"Generate daily sales report and email to team"
Step 4: Deploy to Production
Once your DAG is ready:
Check for accuracy
Optional validation
One-click deployment
Track in Airflow UI
What's Next?
Need Help?
Can't find what you're looking for? Check our Support Center or contact us at [email protected].