Course Outline
Introduction to Apache Airflow
- Defining workflow orchestration
- Key features and advantages of Apache Airflow
- Overview of Airflow 2.x enhancements and ecosystem
Architecture and Fundamental Concepts
- Understanding scheduler, web server, and worker processes
- Exploring DAGs, tasks, and operators
- Executors and backend options (Local, Celery, Kubernetes)
Installation and Configuration
- Deploying Airflow in local and cloud-based environments
- Configuring Airflow using various executors
- Setting up metadata databases and external connections
Utilizing the Airflow UI and CLI
- Navigating the Airflow web interface
- Tracking DAG executions, task status, and logs
- Leveraging the Airflow CLI for administrative tasks
Creating and Managing DAGs
- Developing DAGs using the TaskFlow API
- Implementing operators, sensors, and hooks
- Managing dependencies and scheduling intervals
Integrating Airflow with Data and Cloud Services
- Connecting to databases, APIs, and message queues
- Executing ETL pipelines through Airflow
- Cloud integrations with AWS, GCP, and Azure operators
Monitoring and Observability
- Accessing task logs and real-time monitoring tools
- Visualizing metrics with Prometheus and Grafana
- Setting up alerting and notifications via email or Slack
Securing Apache Airflow
- Implementing Role-Based Access Control (RBAC)
- Configuring authentication with LDAP, OAuth, and SSO
- Managing secrets using Vault and cloud secret stores
Scaling Apache Airflow
- Managing parallelism, concurrency, and task queues
- Utilizing CeleryExecutor and KubernetesExecutor
- Deploying Airflow on Kubernetes using Helm
Production Best Practices
- Applying version control and CI/CD workflows to DAGs
- Testing and debugging DAG logic
- Ensuring reliability and performance at scale
Troubleshooting and Optimization
- Diagnosing failed DAGs and tasks
- Improving DAG execution performance
- Identifying common pitfalls and strategies to avoid them
Conclusion and Future Directions
Requirements
- Proficiency in Python programming
- Familiarity with data engineering or DevOps principles
- A solid understanding of ETL processes or workflow orchestration
Target Audience
- Data scientists
- Data engineers
- DevOps and infrastructure engineers
- 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.
