Course Outline
Introduction to Apache Airflow
- Understanding workflow orchestration
- Key features and advantages of Apache Airflow
- Improvements in Airflow 2.x and an overview of its ecosystem
Architecture and Core Concepts
- Scheduler, web server, and worker processes
- DAGs, tasks, and operators
- Executors and backends (Local, Celery, Kubernetes)
Installation and Configuration
- Setting up Airflow in local and cloud environments
- Configuring Airflow with diverse executors
- Establishing metadata databases and external connections
Utilizing the Airflow UI and CLI
- Exploring the Airflow web interface
- Monitoring DAG executions, tasks, and logs
- Administering via the Airflow CLI
Creating and Managing DAGs
- Building DAGs with the TaskFlow API
- Utilizing operators, sensors, and hooks
- Managing dependencies and scheduling intervals
Airflow Integration with Data and Cloud Services
- Connecting to databases, APIs, and message queues
- Executing ETL pipelines through Airflow
- Cloud integrations: AWS, GCP, and Azure operators
Monitoring and Observability
- Task logging and real-time monitoring
- Metrics tracking with Prometheus and Grafana
- Alerting and notifications via email or Slack
Securing Apache Airflow
- Role-based access control (RBAC)
- Authentication via LDAP, OAuth, and SSO
- Secrets management using Vault and cloud secret stores
Scaling Apache Airflow
- Parallelism, concurrency, and task queues
- Utilizing CeleryExecutor and KubernetesExecutor
- Deploying Airflow on Kubernetes with Helm
Production Best Practices
- Version control and CI/CD implementation for DAGs
- Testing and debugging DAG workflows
- Maintaining reliability and performance at scale
Troubleshooting and Optimization
- Diagnosing failed DAGs and tasks
- Enhancing DAG performance
- Common pitfalls and strategies for avoidance
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Familiarity with data engineering or DevOps principles
- Comprehension 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.
