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Course Outline
Advanced Deployment of Apache Airflow
- Deploying Apache Airflow on cloud platforms (AWS, Azure, GCP).
- Containerizing Airflow using 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.
- Utilizing tools such as Prometheus and Grafana for system monitoring.
- Establishing 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 fundamental Apache Airflow concepts, including DAG creation and task management.
- Knowledge of CI/CD pipelines and DevOps practices.
- Familiarity with cloud environments and containerization technologies (e.g., Docker, Kubernetes).
Audience
- DevOps engineers.
- Infrastructure managers.
- Cloud specialists.
21 Hours
Testimonials (1)
The instructor adapted the training to the participants’ level and responded to all questions. He was very communicative, and it was easy to interact with him. I really appreciated the format of the training, which included many practical exercises. Overall, it was a very engaging and well-organized session.
