Course Outline
Introduction to Apache Airflow
- Understanding workflow orchestration
- Primary features and advantages of Apache Airflow
Architecture and Core Concepts
- Scheduler, web server, and worker process architecture
- DAGs, tasks, and operator components
- Executors and backends (Local, Celery, Kubernetes)
Installation and Setup
- Deploying Airflow in local and cloud-based environments
- Configuration of Airflow using various executors
- Establishing metadata databases and connection settings
Navigating the Airflow UI and CLI
- Exploring the features of the Airflow web interface
- Tracking DAG execution, task status, and logs
- Utilizing the Airflow CLI for administrative tasks
Authoring and Managing DAGs
- Building DAGs utilizing the TaskFlow API
- Implementing operators, sensors, and hooks
- Managing task dependencies and scheduling frequencies
Integrating Airflow with Data and Cloud Services
- Linking Airflow to databases, APIs, and message queues
- Executing ETL workflows via Airflow
- Integrations with cloud platforms: AWS, GCP, and Azure operators
Monitoring and Observability
- Reviewing task logs and real-time system status
- Monitoring metrics using Prometheus and Grafana
- Setting up alerts and notifications via email or Slack
Securing Apache Airflow
- Enforcing Role-based access control (RBAC)
- Configuring authentication through LDAP, OAuth, and SSO
- Managing secrets using Vault and cloud-native secret stores
Scaling Apache Airflow
- Optimizing parallelism, concurrency, and task queuing
- Leveraging CeleryExecutor and KubernetesExecutor for scale
- Deploying Airflow on Kubernetes clusters via Helm
Best Practices for Production
- Implementing version control and CI/CD pipelines for DAGs
- Strategies for testing and debugging DAGs
- Ensuring high reliability and performance in large-scale operations
Troubleshooting and Optimization
- Diagnosing issues with failed DAGs and tasks
- Enhancing DAG execution performance
- Identifying common pitfalls and strategies to prevent them
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Knowledge of data engineering or DevOps principles
- Basic understanding of ETL processes or workflow orchestration
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.
