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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

  • Proficiency in Apache Airflow fundamentals, including DAG creation and task management.
  • Understanding of CI/CD pipelines and DevOps practices.
  • Familiarity with cloud environments and containerization technologies (e.g., Docker, Kubernetes).

Target Audience

  • DevOps engineers.
  • Infrastructure managers.
  • Cloud specialists.
 21 Hours

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