Apache Airflow: Building and Managing Data Pipelines Training Course

Introduction to Apache Airflow

• Understanding the concept of workflow orchestration
• Core features and advantages of Apache Airflow
• Enhancements in Airflow 2.x and an overview of the surrounding ecosystem

Architecture and Fundamental Concepts

• Components including the scheduler, web server, and worker processes
• The structure of DAGs, tasks, and operators
• Executors and backend options (Local, Celery, Kubernetes)

Installation and Configuration

• Setting up Airflow in both local and cloud-based settings
• Configuring Airflow to work with various executors
• Establishing metadata databases and service connections

Exploring the Airflow UI and CLI

• Navigating the Airflow web interface
• Tracking DAG executions, task statuses, and logs
• Utilizing the Airflow command-line interface for administrative tasks

Creating and Managing DAGs

• Building DAGs using the modern TaskFlow API
• Leveraging operators, sensors, and hooks effectively
• Managing task dependencies and defining scheduling intervals

Integrating Airflow with Data and Cloud Services

• Establishing connections to databases, APIs, and message queues
• Executing ETL workflows through Airflow
• Cloud-specific integrations including operators for AWS, GCP, and Azure

Monitoring and Observability

• Accessing task logs and performing real-time monitoring
• Implementing metrics collection using Prometheus and visualization via Grafana
• Setting up alerting and notification systems via email or Slack

Security in Apache Airflow

• Implementing Role-Based Access Control (RBAC)
• Configuring authentication methods such as LDAP, OAuth, and SSO
• Managing secrets using Vault or cloud-native secret stores

Scaling Apache Airflow

• Managing parallelism, concurrency limits, and task queues
• Utilizing CeleryExecutor and KubernetesExecutor for scale
• Deploying Airflow on Kubernetes utilizing Helm charts

Production Best Practices

• Managing version control and CI/CD pipelines for DAGs
• Strategies for testing and debugging DAG implementations
• Ensuring long-term reliability and performance at scale

Troubleshooting and Performance Optimization

• Diagnosing failures in DAGs and individual tasks
• Techniques for optimizing DAG execution speed
• Identifying common pitfalls and strategies to mitigate them

Conclusion and Future Directions