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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
Testimonials (1)
la facilidad de manejo de las maquinas virtuales .... muy bien