Course Outline
Introduction to Apache Airflow
- Defining workflow orchestration
- Core advantages and capabilities of Apache Airflow
- Overview of enhancements in Airflow 2.x and its ecosystem
Architectural Foundations and Core Principles
- Components: Scheduler, web server, and worker processes
- Understanding DAGs, tasks, and operators
- Executable backends: Local, Celery, and Kubernetes
Deployment and Configuration
- Installing Airflow in on-premise and cloud-based setups
- Tuning Airflow configurations for various executors
- Initializing metadata stores and external connections
Interacting with the Airflow Interface and Command Line
- Guided tour of the Airflow web dashboard
- Tracking DAG executions, individual tasks, and logs
- Leveraging the Airflow CLI for administrative tasks
DAG Development and Management
- Constructing DAGs utilizing the TaskFlow API
- Utilizing operators, sensors, and integration hooks
- Handling task dependencies and scheduling frequencies
Connecting Airflow to Data and Cloud Ecosystems
- Linking with databases, REST APIs, and messaging queues
- Executing ETL workflows via Airflow
- Cloud-specific integrations: AWS, GCP, and Azure operators
Monitoring and Observability Strategies
- Accessing task logs and real-time status updates
- Collecting metrics using Prometheus and visualizing with Grafana
- Configuring alerts and notifications via email or Slack
Strengthening Apache Airflow Security
- Implementing Role-Based Access Control (RBAC)
- Managing authentication through LDAP, OAuth, and Single Sign-On
- Handling sensitive data with Vault and cloud-native secret stores
Achieving Scale with Apache Airflow
- Managing parallelism, concurrency limits, and task queues
- Deploying with CeleryExecutor and KubernetesExecutor
- Installing Airflow on Kubernetes using Helm charts
Production-Grade Best Practices
- Integrating version control and CI/CD pipelines for DAGs
- Rigorous testing and debugging procedures for DAGs
- Ensuring sustained reliability and performance during peak loads
Diagnosis and Performance Tuning
- Investigating failed DAGs and specific task errors
- Enhancing DAG execution efficiency
- Identifying frequent mistakes and prevention strategies
Recap and Future Pathways
Requirements
- Proficiency in Python programming
- Knowledge of data engineering or DevOps principles
- Familiarity with 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.
