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Course Outline
Recap of Apache Airflow Fundamentals
- Core concepts: DAGs, operators, and execution flow
- Airflow architecture and components
- Understanding advanced use cases and workflows
Creating Custom Operators
- Understanding the anatomy of an Airflow operator
- Developing custom operators for specific tasks
- Testing and debugging custom operators
Custom Hooks and Sensors
- Implementing hooks for external system integration
- Creating sensors for monitoring external triggers
- Enhancing workflow interactivity with custom sensors
Developing Airflow Plugins
- Understanding the plugin architecture
- Designing plugins to extend Airflow functionality
- Best practices for managing and deploying plugins
Integrating Airflow with External Systems
- Connecting Airflow to databases, APIs, and cloud services
- Using Airflow for ETL workflows and real-time data processing
- Managing dependencies between Airflow and external systems
Advanced Debugging and Monitoring
- Using Airflow logs and metrics for troubleshooting
- Configuring alerts and notifications for workflow issues
- Leveraging external monitoring tools with Airflow
Optimizing Performance and Scalability
- Scaling Airflow with Celery and Kubernetes Executors
- Optimizing resource utilization in complex workflows
- Strategies for high availability and fault tolerance
Case Studies and Real-World Applications
- Exploring advanced use cases in data engineering and DevOps
- Case study: Custom operator implementation for large-scale ETL
- Best practices for managing enterprise-level workflows
Summary and Next Steps
Requirements
- Strong understanding of Apache Airflow basics, including DAGs, operators, and execution architecture
- Proficiency in Python programming
- Experience with integrating data systems and workflow orchestration
Audience
- Data engineers
- DevOps engineers
- Software architects
21 Hours
