Advanced Apache Airflow: Custom Operators and Plugins

• 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

Apache Airflow is a powerful open-source platform for orchestrating workflows and automating complex data pipelines.

This instructor-led, live training (online or onsite) is aimed at advanced-level participants who wish to create custom operators, sensors, and plugins in Apache Airflow, and integrate these with existing data systems for advanced automation and monitoring.

By the end of this training, participants will be able to:

• Develop custom operators, hooks, and sensors tailored to specific workflows.
• Design and implement Airflow plugins to extend functionality.
• Integrate Airflow workflows with external systems and services.
• Optimize and monitor workflows using advanced debugging tools.
• Leverage best practices for managing large-scale Airflow deployments.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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