Course Outline
Introduction to Apache Airflow
- Defining workflow orchestration
- Core features and advantages of Apache Airflow
- Enhancements in Airflow 2.x and an overview of its ecosystem
Architecture and Fundamental Concepts
- Scheduler, web server, and worker components
- Understanding DAGs, tasks, and operators
- Executors and backends (Local, Celery, Kubernetes)
Installation and Configuration
- Deploying Airflow in local and cloud-based environments
- Tuning Airflow settings for various executors
- Establishing metadata databases and service connections
Utilizing the Airflow UI and CLI
- Exploring the capabilities of the Airflow web interface
- Tracking DAG executions, tasks, and associated logs
- Leveraging the Airflow CLI for administrative tasks
Creating and Managing DAGs
- Building DAGs using the TaskFlow API
- Incorporating operators, sensors, and hooks
- Handling dependencies and defining scheduling intervals
Integrating Airflow with Data and Cloud Platforms
- Linking to databases, APIs, and message queues
- Executing ETL pipelines through Airflow
- Cloud-specific integrations: AWS, GCP, and Azure operators
Monitoring and Observability
- Accessing task logs and real-time monitoring data
- Visualizing metrics using Prometheus and Grafana
- Configuring alerts and notifications via email or Slack
Securing Apache Airflow
- Implementing Role-Based Access Control (RBAC)
- Setting up authentication via LDAP, OAuth, and SSO
- Managing secrets using Vault and cloud-native secret stores
Scaling Apache Airflow
- Managing parallelism, concurrency, and task queues
- Utilizing CeleryExecutor and KubernetesExecutor
- Deploying Airflow on Kubernetes via Helm
Production Best Practices
- Applying version control and CI/CD to DAGs
- Conducting testing and debugging of DAGs
- Maintaining reliability and performance at scale
Troubleshooting and Optimization
- Diagnosing failed DAGs and tasks
- Enhancing DAG performance
- Identifying common pitfalls and strategies to avoid them
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Knowledge of data engineering or DevOps principles
- Familiarity with 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.
