Course Outline
Introduction to Apache Airflow
- Defining workflow orchestration
- Key capabilities and advantages of Apache Airflow
- Enhancements in Airflow 2.x and an ecosystem overview
Architectural Foundations and Core Principles
- Scheduler, web server, and worker processes
- DAGs, tasks, and operators
- Executors and backend options (Local, Celery, Kubernetes)
Deployment and Configuration
- Installing Airflow in both local and cloud-based environments
- Configuring Airflow with varied executors
- Establishing metadata databases and external connections
Interaction with the Airflow UI and CLI
- Exploring the features of the Airflow web interface
- Tracking DAG runs, individual tasks, and logs
- Utilizing the Airflow CLI for administrative tasks
Creation and Administration of DAGs
- Building DAGs via the TaskFlow API
- Utilizing operators, sensors, and hooks
- Managing dependencies and scheduling intervals
Integration of Airflow with Data and Cloud Platforms
- Establishing connections to databases, APIs, and message queues
- Executing ETL pipelines through Airflow
- Cloud connectivity: AWS, GCP, and Azure operators
Monitoring and Observability Strategies
- Analyzing task logs and performing real-time monitoring
- Generating metrics using Prometheus and Grafana
- Setting up alerting and notifications via email or Slack
Security Measures for Apache Airflow
- Implementing role-based access control (RBAC)
- Configuring authentication through LDAP, OAuth, and SSO
- Managing secrets using Vault and cloud secret stores
Scaling Apache Airflow Operations
- 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 DAGs effectively
- Sustaining reliability and performance at scale
Troubleshooting and Performance Optimization
- Diagnosing failed DAGs and tasks
- Enhancing DAG execution performance
- Identifying common pitfalls and strategies to avoid them
Recap and Recommended Next Steps
Requirements
- Proficiency in Python programming
- Working knowledge of data engineering or DevOps principles
- Conceptual 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.
