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Course Outline

Apache Airflow Fundamentals Review

  • Core components: DAGs, tasks, and operators
  • Overview of Apache Airflow architecture and essential parts
  • Revisiting typical use cases and workflow structures

Workflow Performance Optimization

  • Detecting performance bottlenecks in Airflow pipelines
  • Applying task-level optimization strategies
  • Utilizing task retries, parallel execution, and concurrency controls

Handling Complex Dependencies

  • Establishing dynamic dependencies within workflows
  • Managing conditional logic and branching paths
  • Effective use of task groups and sub-DAGs

Advanced Apache Airflow Capabilities

  • Developing custom operators and hooks
  • Implementing sensors for external event triggers
  • Integrating third-party services and plugins

Scaling Apache Airflow Architectures

  • Strategies for horizontal and vertical scaling
  • Deploying Celery Executors for distributed processing
  • Best practices for scaling deployments in cloud infrastructure

Workflow Monitoring and Debugging

  • Setting up logging mechanisms and alert systems
  • Utilizing the Airflow UI and CLI for diagnostic tasks
  • Identifying and remedifying frequent deployment issues

Apache Airflow Security

  • Configuring authentication and access control protocols
  • Safeguarding sensitive data and environment configurations
  • Establishing audit trails for workflow activities

Enterprise Applications and Best Practices

  • Designing resilient workflows for production environments
  • Applying Apache Airflow to data engineering and ETL pipelines
  • Analyzing real-world case studies of scalable Airflow implementations

Wrap-up and Recommendations

Requirements

  • Foundational understanding of Apache Airflow
  • Proficiency in Python programming and a solid grasp of workflow orchestration principles
  • Hands-on experience deploying and managing applications in Linux-based environments

Target Audience

  • Data Engineers
  • DevOps Specialists
  • Software Developers
 21 Hours

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