Course Outline
Introduction to Apache Airflow
- Defining workflow orchestration
- Core features and advantages of Apache Airflow
- Enhancements in Airflow 2.x and ecosystem landscape
Architecture and Fundamental Concepts
- Scheduler, web server, and worker component processes
- DAGs, individual tasks, and operators
- Executing backends (Local, Celery, Kubernetes) and executor types
Installation and Configuration
- Deploying Airflow in local and cloud-based environments
- Tuning Airflow settings for various executors
- Establishing metadata databases and external connections
Interacting with the Airflow Interface
- Exploring the Airflow web dashboard
- Tracking DAG executions, task status, and logs
- Utilizing the Airflow command-line interface for administration
Creating and Managing DAGs
- Building DAGs via the TaskFlow API
- Leveraging operators, sensors, and hooks
- Handling dependencies and defining scheduling intervals
Connecting Airflow with Data and Cloud Platforms
- Linking to databases, APIs, and message queues
- Executing ETL pipelines using Airflow
- Cloud connectivity: AWS, GCP, and Azure operators
Monitoring and Observability
- Analyzing task logs and real-time performance
- Integrating metrics with Prometheus and Grafana
- Configuring alerts and notifications via email or Slack
Enhancing Apache Airflow Security
- Implementing Role-Based Access Control (RBAC)
- 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 using Helm
Production Best Practices
- Implementing version control and CI/CD pipelines for DAGs
- Testing and debugging DAG logic
- Ensuring reliability and optimal performance at scale
Troubleshooting and Performance Tuning
- Diagnosing failed DAGs and specific task errors
- Optimizing overall DAG execution speed
- Identifying and avoiding common pitfalls
Conclusion and Future Directions
Requirements
- Proficiency in Python programming
- Working knowledge of data engineering or DevOps principles
- Basic understanding of ETL processes or workflow orchestration
Target 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.
