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Course Outline
Advanced Deployment of Apache Airflow
- Deploying Apache Airflow on cloud platforms (AWS, Azure, GCP)
- Containerizing Airflow with Docker and Kubernetes
- Configuring Airflow for high availability and fault tolerance
CI/CD Pipelines for Apache Airflow
- Automating DAG testing and deployment
- Integrating Airflow with CI/CD tools (e.g., Jenkins, GitHub Actions)
- Managing workflow versioning and updates
Monitoring and Logging
- Implementing robust logging practices for workflows
- Using tools like Prometheus and Grafana for system monitoring
- Setting up alerting mechanisms for failure scenarios
Performance Optimization and Scaling
- Tuning Airflow configurations for optimal performance
- Scaling Airflow deployments with Celery executors
- Handling large-scale workflow orchestration
Security and Access Control
- Implementing role-based access control (RBAC) in Airflow
- Securing Airflow environments and workflows
- Best practices for managing sensitive data in workflows
Case Studies and Practical Applications
- Real-world examples of Airflow for DevOps automation
- Hands-on exercise: Deploying Airflow with CI/CD and monitoring tools
- Discussion on challenges and solutions in DevOps workflow orchestration
Summary and Next Steps
Requirements
- Experience with Apache Airflow basics, including DAG creation and task management
- Knowledge of CI/CD pipelines and DevOps practices
- Familiarity with cloud environments and containerization (e.g., Docker, Kubernetes)
Audience
- DevOps engineers
- Infrastructure managers
- Cloud specialists
21 Hours
