Course Outline
Introduction to Apache Airflow
- The concept of workflow orchestration
- Core features and advantages of Apache Airflow
- Enhancements in Airflow 2.x and an overview of its ecosystem
Architecture and Fundamental Concepts
- Understanding scheduler, web server, and worker processes
- Structure of DAGs, tasks, and operators
- Executors and backend options (Local, Celery, Kubernetes)
Installation and Configuration
- Setting up Airflow in both local and cloud environments
- Configuring Airflow with various executor types
- Initializing metadata databases and establishing connections
Navigating the Airflow Interface and Command Line
- Exploring the capabilities of the Airflow web interface
- Monitoring DAG executions, tasks, and log outputs
- Utilizing the Airflow CLI for administrative tasks
Creating and Managing DAGs
- Building DAGs using the TaskFlow API
- Leveraging operators, sensors, and hooks effectively
- Managing task dependencies and scheduling intervals
Integrating Airflow with Data and Cloud Ecosystems
- Connecting to databases, external APIs, and message queues
- Executing ETL workflows within Airflow
- Cloud-specific integrations: AWS, GCP, and Azure operators
Monitoring and Observability Strategies
- Analyzing task logs and performing real-time monitoring
- Implementing metrics tracking with Prometheus and Grafana
- Setting up alerting and notifications via email or Slack
Enhancing Apache Airflow Security
- Implementing Role-based access control (RBAC)
- Authentication methods using LDAP, OAuth, and SSO
- Managing secrets with Vault and cloud-based secret stores
Scaling Apache Airflow Operations
- Managing parallelism, concurrency, and task queues
- Utilizing CeleryExecutor and KubernetesExecutor
- Deploying Airflow on Kubernetes using Helm
Best Practices for Production Environments
- Applying version control and CI/CD pipelines to DAGs
- Techniques for testing and debugging DAGs
- Maintaining reliability and performance as scale increases
Troubleshooting and Performance Optimization
- Diagnosing failed DAGs and individual tasks
- Optimizing DAG execution performance
- Identifying common pitfalls and strategies to avoid them
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Familiarity with data engineering or DevOps principles
- Foundational understanding of ETL processes or workflow orchestration
Target Audience
- Data scientists
- Data engineers
- DevOps and infrastructure specialists
- Software developers
Testimonials (7)
The instructor adapted the training to the participants’ level and responded to all questions. He was very communicative, and it was easy to interact with him. I really appreciated the format of the training, which included many practical exercises. Overall, it was a very engaging and well-organized session.
Jacek Chlopik - ZAKLAD UBEZPIECZEN SPOLECZNYCH
Course - Apache Airflow: Building and Managing Data Pipelines
The training was spot on. Very useful theory and exercices.
Vladimir - PUBLIC COURSE
Course - Apache Airflow
The training was spot on in all aspects. Usefull theoretical aspects and exercises.
Vladimir - PUBLIC COURSE
Course - Apache Airflow
The training was spot on in all aspects. Usefull theoretical aspects and exercises.
Vladimir - PUBLIC COURSE
Course - Apache Airflow
The training was spot on in all aspects. Usefull theoretical aspects and exercises.
Vladimir - PUBLIC COURSE
Course - Apache Airflow
The training was spot on in all aspects. Usefull theoretical aspects and exercises.
Vladimir - PUBLIC COURSE
Course - Apache Airflow
The training was spot on in all aspects. Usefull theoretical aspects and exercises.
