Intermediate Apache Airflow: Optimizing and Scaling Workflows

• 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

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level participants who wish to optimize workflow performance, handle complex dependencies, and scale Apache Airflow deployments for larger datasets and enterprise use cases.

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

• Optimize Apache Airflow workflows for better performance and reliability.
• Manage and troubleshoot complex workflow dependencies.
• Leverage advanced Airflow features, including custom operators and sensors.
• Scale Apache Airflow for handling larger data sets and distributed systems.
• Implement best practices for monitoring, logging, and security in Airflow environments.

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.

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