Course Outline
Introduction to Apache Airflow
- The concept of workflow orchestration
- Principal features and advantages of Apache Airflow
- Enhancements in Airflow 2.x and an overview of the ecosystem
Architecture and Fundamental Concepts
- Scheduler, web server, and worker processes
- DAGs, tasks, and operators
- Executors and backends (Local, Celery, Kubernetes)
Installation and Configuration
- Deploying Airflow in local and cloud environments
- Configuring Airflow with various executors
- Establishing metadata databases and connections
Navigating the Airflow UI and Command Line Interface
- Exploring the Airflow web interface
- Supervising DAG runs, tasks, and logs
- Utilizing the Airflow CLI for administrative tasks
Creating and Managing DAGs
- Developing DAGs using the TaskFlow API
- Employing 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 via Airflow
- Cloud integrations: AWS, GCP, and Azure operators
Monitoring and Observability
- Task logs and real-time supervision
- Metrics management with Prometheus and Grafana
- Alerting and notifications via email or Slack
Securing Apache Airflow
- Role-based access control (RBAC)
- Authentication using LDAP, OAuth, and SSO
- Secrets management with Vault and cloud secret stores
Scaling Apache Airflow
- Parallelism, concurrency, and task queues
- Leveraging CeleryExecutor and KubernetesExecutor
- Deploying Airflow on Kubernetes using Helm
Production Best Practices
- Version control and CI/CD for DAGs
- Testing and debugging DAGs
- Ensuring reliability and performance at scale
Troubleshooting and Optimization
- Diagnosing failed DAGs and tasks
- Improving DAG performance
- Common pitfalls and strategies to avoid them
Summary and Future Steps
Requirements
- Proficiency in Python programming
- Familiarity with data engineering or DevOps principles
- A solid grasp 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.
