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Course Outline
Advanced Deployment of Apache Airflow
- Deploying Apache Airflow on major 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 workflows.
- Integrating Airflow with CI/CD platforms (e.g., Jenkins, GitHub Actions).
- Managing versioning and updates for workflows.
Monitoring and Logging
- Establishing rigorous logging standards for workflows.
- Leveraging tools such as Prometheus and Grafana for system oversight.
- Configuring alert mechanisms for potential failure scenarios.
Performance Optimization and Scaling
- Tuning Airflow configurations for peak performance.
- Scaling Airflow deployments via Celery executors.
- Managing large-scale workflow orchestration.
Security and Access Control
- Implementing Role-Based Access Control (RBAC) in Airflow.
- Safeguarding Airflow environments and workflows.
- Adopting best practices for handling sensitive data within workflows.
Case Studies and Practical Applications
- Real-world examples illustrating Airflow's role in DevOps automation.
- Practical exercise: Deploying Airflow alongside CI/CD and monitoring tools.
- Discussion on challenges and solutions in DevOps workflow orchestration.
Summary and Next Steps
Requirements
- Hands-on experience with core Apache Airflow concepts, such as DAG creation and task management.
- Proficiency in CI/CD pipelines and DevOps methodologies.
- Understanding of cloud environments and containerization technologies (e.g., Docker, Kubernetes).
Target 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.
