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Course Outline
Advanced Apache Airflow Deployment
- Deploying Apache Airflow on major cloud platforms (AWS, Azure, GCP)
- Containerizing Airflow using Docker and Kubernetes
- Configuring Airflow for high availability and fault resilience
CI/CD Pipelines for Apache Airflow
- Automating the testing and deployment of DAGs
- Integrating Airflow with CI/CD tools (e.g., Jenkins, GitHub Actions)
- Managing workflow version control and updates
Monitoring and Logging
- Implementing rigorous logging standards for workflows
- Leveraging tools such as Prometheus and Grafana for system oversight
- Establishing alerting mechanisms for failure management
Performance Optimization and Scaling
- Tuning Airflow settings for peak performance
- Scaling Airflow instances using Celery executors
- Managing large-scale workflow orchestration
Security and Access Control
- Enacting role-based access control (RBAC) within Airflow
- Safeguarding Airflow environments and associated workflows
- Applying best practices for handling sensitive data in workflows
Case Studies and Practical Applications
- Real-world examples of Airflow in DevOps automation
- Hands-on exercise: Deploying Airflow with CI/CD and monitoring integrations
- Discussing common challenges and effective solutions in DevOps workflow orchestration
Summary and Next Steps
Requirements
- Foundational proficiency with Apache Airflow, including DAG construction and task oversight
- Understanding of CI/CD pipelines and core DevOps principles
- Working knowledge of cloud environments and container technologies (such as Docker and 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.
