Course Outline

Review of Apache Airflow Basics

  • Core concepts: DAGs, tasks, and operators
  • Airflow architecture and components
  • Recap of common use cases and workflows

Optimizing Workflow Performance

  • Identifying bottlenecks in Airflow pipelines
  • Task-level optimization techniques
  • Leveraging task retries, parallelism, and concurrency

Managing Complex Dependencies

  • Defining dynamic dependencies in workflows
  • Handling conditional and branching workflows
  • Using task groups and sub-DAGs effectively

Advanced Features in Apache Airflow

  • Creating custom operators and hooks
  • Implementing sensors for external triggers
  • Integrating third-party services and plugins

Scaling Apache Airflow Deployments

  • Horizontal and vertical scaling approaches
  • Using Celery Executors for distributed execution
  • Best practices for scaling in cloud environments

Monitoring and Debugging Workflows

  • Configuring logging and alerts for workflow monitoring
  • Using the Airflow UI and CLI for troubleshooting
  • Identifying and resolving common issues in Airflow deployments

Securing Apache Airflow

  • Authentication and access control in Airflow
  • Protecting sensitive data and environment configurations
  • Implementing audit trails for workflows

Enterprise Use Cases and Best Practices

  • Designing robust workflows for production environments
  • Leveraging Airflow for data engineering and ETL pipelines
  • Exploring real-world case studies of scalable Airflow deployments

Summary and Next Steps

Requirements

  • Basic knowledge of Apache Airflow
  • Familiarity with Python programming and workflow orchestration concepts
  • Experience in managing and deploying applications on Linux environments

Audience

  • Data engineers
  • DevOps professionals
  • Software developers
 21 Hours

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories