Apache Airflow: Building and Managing Data Pipelines Training Course

Introduction to Apache Airflow

• Understanding the concept of workflow orchestration.
• Core features and advantages of using Apache Airflow.
• Overview of enhancements in Airflow 2.x and its broader ecosystem.

Architectural Foundations and Core Concepts

• Roles of the scheduler, web server, and worker processes.
• Details on DAGs, individual tasks, and operators.
• Exploration of executors and backend options such as Local, Celery, and Kubernetes.

Deployment and Configuration

• Installing Airflow in both local and cloud-based settings.
• Tuning Airflow configurations to suit different executor types.
• Establishing metadata databases and defining external connections.

Utilizing the Airflow Interface and Command Line

• Navigating the features of the Airflow web interface.
• Tracking DAG executions, task statuses, and reviewing logs.
• Leveraging the Airflow Command Line Interface for administrative tasks.

Creating and Maintaining DAGs

• Building DAGs utilizing the modern TaskFlow API.
• Applying various operators, sensors, and hooks effectively.
• Defining task dependencies and configuring scheduling intervals.

Connecting Airflow to Data and Cloud Platforms

• Linking Airflow with databases, external APIs, and message queues.
• Orchestrating ETL pipelines using Airflow capabilities.
• Implementing cloud integrations with AWS, GCP, and Azure operators.

Monitoring and Observability Strategies

• Analyzing task logs and performing real-time performance monitoring.
• Setting up metrics collection using Prometheus and visualizing them in Grafana.
• Configuring alerting systems and notifications via email or Slack.

Enhancing Apache Airflow Security

• Implementing Role-Based Access Control (RBAC) policies.
• Setting up authentication methods including LDAP, OAuth, and Single Sign-On (SSO).
• Managing secrets using Vault and cloud-native secret stores.

Scaling Apache Airflow Operations

• Managing parallelism, concurrency limits, and task queue management.
• Utilizing CeleryExecutor and KubernetesExecutor for distributed processing.
• Deploying Airflow instances on Kubernetes using Helm charts.

Production Best Practices

• Integrating version control and CI/CD pipelines for DAG management.
• Developing strategies for testing and debugging complex DAGs.
• Ensuring high reliability and optimal performance in large-scale environments.

Troubleshooting and Performance Optimization

• Diagnosing and resolving issues with failed DAGs and tasks.
• Techniques for optimizing DAG execution speed and resource usage.
• Identifying common pitfalls and strategies to prevent them.

Course Recap and Future Pathways