Course Outline
Introduction to Apache Airflow
- Understanding workflow orchestration
- Key features and advantages of Apache Airflow
- Overview of Airflow 2.x enhancements and its ecosystem
Architecture and Core Concepts
- Roles of scheduler, web server, and worker processes
- Concepts of DAGs, tasks, and operators
- Executors and backend options (Local, Celery, Kubernetes)
Installation and Configuration
- Setting up Airflow in local and cloud-based environments
- Configuring Airflow with various executor types
- Establishing metadata databases and connection settings
Interacting with the Airflow UI and CLI
- Exploring the Airflow web interface
- Monitoring DAG executions, tasks, and logs
- Utilizing the Airflow CLI for administrative tasks
Creating and Managing DAGs
- Building DAGs using the TaskFlow API
- Applying operators, sensors, and hooks
- Handling dependencies and defining scheduling intervals
Integrating Airflow with Data and Cloud Services
- Establishing connections to databases, APIs, and message queues
- Executing ETL pipelines through Airflow
- Cloud integrations: Operators for AWS, GCP, and Azure
Monitoring and Observability
- Reviewing task logs and real-time monitoring
- Implementing metrics with Prometheus and Grafana
- Setting up alerting and notifications via email or Slack
Securing Apache Airflow
- Implementing Role-Based Access Control (RBAC)
- Authentication methods using LDAP, OAuth, and SSO
- Managing secrets with Vault and cloud-based secret stores
Scaling Apache Airflow
- Managing parallelism, concurrency, and task queues
- Utilizing CeleryExecutor and KubernetesExecutor
- Deploying Airflow on Kubernetes using Helm
Best Practices for Production Environments
- Implementing version control and CI/CD for DAGs
- Testing and debugging DAG structures
- Ensuring reliability and performance at scale
Troubleshooting and Optimization
- Diagnosing failed DAGs and individual tasks
- Optimizing DAG execution performance
- Identifying common pitfalls and strategies to avoid them
Summary and Future Directions
Requirements
- Proficiency in Python programming
- Familiarity with data engineering or DevOps principles
- An understanding 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.
