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 the ecosystem
Architecture and Fundamental Concepts
- Scheduler, web server, and worker processes
- Understanding DAGs, tasks, and operators
- Executors and backends, including Local, Celery, and Kubernetes
Installation and Configuration
- Installing Airflow in both local and cloud-based settings
- Configuring Airflow with various executor types
- Establishing metadata databases and system connections
Interacting with the Airflow UI and CLI
- Exploring the functionalities of the Airflow web interface
- Tracking DAG executions, tasks, and associated logs
- Utilizing the Airflow CLI for administrative tasks
Developing and Managing DAGs
- Building DAGs using the TaskFlow API
- Implementing operators, sensors, and hooks
- Handling dependencies and defining scheduling intervals
Integration with Data and Cloud Platforms
- Connecting to databases, APIs, and message queues
- Executing ETL workflows via Airflow
- Cloud-specific integrations, including AWS, GCP, and Azure operators
Monitoring and Observability
- Accessing task logs and real-time performance monitoring
- Visualizing metrics using Prometheus and Grafana
- Configuring alerts and notifications via email or Slack
Enhancing Apache Airflow Security
- Implementing Role-Based Access Control (RBAC)
- Managing authentication through LDAP, OAuth, and SSO
- Secure secret management using Vault and cloud-native secret stores
Scaling Apache Airflow
- Optimizing parallelism, concurrency, and task queues
- Leveraging CeleryExecutor and KubernetesExecutor
- Deploying Airflow on Kubernetes using Helm
Production Best Practices
- Incorporating version control and CI/CD pipelines for DAGs
- Techniques for testing and debugging DAGs
- Ensuring reliability and performance optimization at scale
Troubleshooting and Performance Tuning
- Diagnosing failed DAGs and individual tasks
- Strategies for optimizing DAG execution speed
- Identifying common pitfalls and preventive measures
Summary and Future Directions
Requirements
- Proficiency in Python programming
- General familiarity with data engineering or DevOps principles
- Basic understanding of ETL processes or workflow orchestration
Intended 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.
