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Duration 21 hours
Course Outline
Review of Apache Airflow Core Principles
- Foundational elements: Directed Acyclic Graphs (DAGs), operators, and task execution sequences
- System architecture and internal components
- Analysis of complex operational scenarios and process designs
Construction of Custom Operators
- Analyzing the structural components of an Airflow operator
- Engineering bespoke operators for specialized operational tasks
- Conducting rigorous testing and diagnostic procedures for custom components
Development of Custom Hooks and Sensors
- Establishing connections to third-party systems via custom hooks
- Building sensors to track and respond to external event triggers
- Improving workflow responsiveness through custom sensor integration
Engineering Airflow Plugins
- Examining the underlying plugin framework architecture
- Architecting plugins to expand core Airflow capabilities
- Adhering to standards for the maintenance and deployment of plugin modules
Integration with External Systems
- Establishing secure links between Airflow, database repositories, API endpoints, and cloud infrastructure
- Utilizing Airflow for extract, transform, and load (ETL) processes and real-time data handling
- Managing interdependencies between Airflow instances and connected external resources
Advanced Diagnostics and Oversight
- Leveraging system logs and performance metrics for fault identification
- Configuring automated alerts and notification systems for process anomalies
- Incorporating third-party monitoring solutions within the Airflow environment
Performance Optimization and Scalability
- Scaling infrastructure using Celery and Kubernetes execution models
- Enhancing resource efficiency in complex process flows
- Implementing strategies for high availability and system resilience
Applied Case Studies and Operational Scenarios
- Evaluating advanced applications in data engineering and DevOps environments
- Case analysis: Implementation of custom operators for large-scale ETL operations
- Standards for the management of enterprise-grade workflow systems
Conclusion and Forward-Looking Actions
Requirements
- Comprehensive knowledge of Apache Airflow fundamentals, including DAGs, operators, and execution architecture
- Demonstrated proficiency in Python programming
- Practical experience with data system integration and workflow orchestration
Target Audience
- Data Engineers
- DevOps Engineers
- Software Architects
Testimonials (1)
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.
