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Course Outline
Advanced Apache Airflow Deployment
- 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 processes.
- Integrating Airflow with CI/CD tools (e.g., Jenkins, GitHub Actions).
- Managing workflow versioning and updates.
Monitoring and Logging
- Establishing robust logging practices for workflows.
- Utilizing tools such as Prometheus and Grafana for system monitoring.
- Configuring alerting mechanisms for failure scenarios.
Performance Optimization and Scaling
- Tuning Airflow configurations for peak performance.
- Scaling Airflow deployments with Celery executors.
- Managing large-scale workflow orchestration.
Security and Access Control
- Implementing role-based access control (RBAC) in Airflow.
- Securing Airflow environments and workflows.
- Adopting best practices for handling 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
- Foundational experience with Apache Airflow, including the creation of DAGs and task management.
- Understanding of CI/CD pipelines and DevOps principles.
- Knowledge of cloud infrastructures 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.
