Course Outline
Overview of Apache Airflow
- Fundamentals of workflow orchestration
- Core capabilities and operational advantages of Apache Airflow
- Enhancements in Airflow 2.x and ecosystem components
Architectural Framework and Key Concepts
- Components: Scheduler, web server, and worker processes
- Directed Acyclic Graphs (DAGs), tasks, and operators
- Execution engines and storage backends (Local, Celery, Kubernetes)
Deployment and Configuration
- Deployment of Airflow in local and cloud-based environments
- Configuration of Airflow for specific execution engines
- Establishment of metadata databases and system connections
Interface Navigation and Command-Line Management
- Utilization of the Airflow web interface
- Oversight of DAG executions, tasks, and log data
- Administrative functions via the Airflow Command Line Interface (CLI)
Development and Management of Directed Acyclic Graphs (DAGs)
- Creation of DAGs using the TaskFlow API
- Application of operators, sensors, and hooks
- Management of task dependencies and scheduling parameters
Integration with Data Infrastructure and Cloud Platforms
- Connectivity to databases, application programming interfaces (APIs), and message queues
- Execution of Extract, Transform, Load (ETL) pipelines
- Cloud platform integrations: Operators for AWS, GCP, and Azure
Monitoring and System Observability
- Access to task logs and real-time system monitoring
- Data metrics collection via Prometheus and Grafana
- Notification systems for alerts using email or Slack
Security Protocols for Apache Airflow
- Implementation of Role-Based Access Control (RBAC)
- Authentication methods including LDAP, OAuth, and Single Sign-On (SSO)
- Secure storage management using Vault and cloud-based secret repositories
Scalability Strategies for Apache Airflow
- Management of parallelism, concurrency levels, and task queues
- Deployment of CeleryExecutor and KubernetesExecutor engines
- kubernetes-based deployment of Airflow using Helm charts
Operational Best Practices for Production Environments
- Implementation of version control and Continuous Integration/Continuous Deployment (CI/CD) pipelines for DAGs
- Procedures for testing and debugging DAG configurations
- sustaining system reliability and performance under scale
Troubleshooting and System Optimization
- Diagnosis of failed DAGs and tasks
- Techniques for optimizing DAG execution performance
- Identification of common operational pitfalls and mitigation strategies
Summary and Recommended Next Steps
Requirements
- Proficiency in Python development
- Knowledge of data engineering and DevOps methodologies
- Competence in ETL processes and workflow orchestration
Intended Audience
- Data scientists
- Data engineers
- DevOps and infrastructure specialists
- Software engineers
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.
