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 Duration 21 hours

Course Outline

Advanced Deployment Strategies for Apache Airflow

  • Implementing Apache Airflow on major cloud platforms (AWS, Azure, GCP)
  • Containerizing Airflow instances using Docker and Kubernetes
  • Configuring Airflow to ensure high availability and fault resilience

CI/CD Integration for Apache Airflow

  • Streamlining DAG testing and release processes through automation
  • Connecting Airflow with CI/CD platforms (e.g., Jenkins, GitHub Actions)
  • Overseeing workflow version control and update cycles

Observability through Monitoring and Logging

  • Establishing rigorous logging protocols for workflow execution
  • Leveraging tools like Prometheus and Grafana for system oversight
  • Configuring alert systems to address failure scenarios

Performance Tuning and Scalability

  • Adjusting Airflow parameters for peak operational efficiency
  • Expanding Airflow capacity using Celery executors
  • Managing orchestration of large-scale workflows

Security Governance and Access Management

  • Applying role-based access control (RBAC) within Airflow
  • Hardening Airflow environments and associated workflows
  • Adopting best practices for handling sensitive data in workflow pipelines

Practical Applications and Case Studies

  • Examining real-world instances of Airflow in DevOps automation
  • Hands-on lab: Deploying Airflow alongside CI/CD and monitoring stacks
  • Analyzing challenges and resolutions in DevOps workflow orchestration

Wrap-up and Future Directions

Requirements

  • Foundational proficiency in Apache Airflow, covering DAG construction and task administration
  • Working knowledge of CI/CD pipelines and DevOps workflows
  • Exposure to cloud platforms and container technologies (such as Docker and Kubernetes)

Intended Audience

  • DevOps Engineers
  • Infrastructure Managers
  • Cloud Specialists

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