Course Outline
Introduction to Apache Airflow
- The concept of workflow orchestration
- Primary features and advantages of Apache Airflow
- Enhancements in Airflow 2.x and an overview of its ecosystem
Core Architecture and Concepts
- Scheduler, web server, and worker component interactions
- Understanding DAGs, tasks, and operators
- Executors and backend options (Local, Celery, Kubernetes)
Installation and Configuration
- Deploying Airflow in local and cloud-based settings
- Adjusting Airflow configurations for various executors
- Initializing metadata databases and external connections
Utilizing the Airflow Interface and Command Line
- Navigating the Airflow web dashboard
- Tracking DAG executions, individual tasks, and log data
- Leveraging the Airflow CLI for administrative tasks
Developing and Administering DAGs
- Constructing DAGs via the TaskFlow API
- Applying operators, sensors, and hooks effectively
- Handling task dependencies and scheduling frequencies
Connecting Airflow to Data and Cloud Platforms
- Establishing links to databases, APIs, and message queues
- Executing ETL workflows through Airflow
- Cloud-specific integrations: AWS, GCP, and Azure operators
Monitoring and Observability Strategies
- Reviewing task logs and real-time status updates
- Collecting metrics using Prometheus and Grafana
- Configuring alerts and notifications via email or Slack
Securing Your Apache Airflow Instance
- Implementing role-based access control (RBAC)
- Authentication methods using LDAP, OAuth, and SSO
- Managing secrets with 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 charts
Production Best Practices
- Applying version control and CI/CD pipelines to DAGs
- Techniques for testing and debugging workflows
- Ensuring reliability and optimal performance at scale
Troubleshooting and Performance Tuning
- Diagnosing failed DAGs and individual tasks
- Strategies to optimize DAG execution speed
- Identifying common pitfalls and methods to prevent them
Recap and Future Directions
Requirements
- Proficiency in Python programming
- Basic knowledge of data engineering or DevOps principles
- General 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.
