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Course Outline

Review of Apache Airflow Fundamentals

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

Target Audience

  • Data engineers.
  • DevOps professionals.
  • Software developers.
 21 Hours

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