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Apache Airflow Notes

  1. Apache Airflow: An open-source platform to programmatically author, schedule, and monitor workflows. It is used to orchestrate complex computational workflows and data processing pipelines.
  2. Core Concepts:
    • DAG (Directed Acyclic Graph): A collection of tasks with directional dependencies, representing a workflow.
    • Task: A unit of work in a DAG, which can be an operator, sensor, or sub-DAG.
    • Operator: Defines a single task in a workflow (e.g., BashOperator, PythonOperator).
    • Sensor: A special type of operator that waits for a certain condition to be met before proceeding.
  3. Key Features:
    • Dynamic Workflows: Workflows are defined as code, making them dynamic and flexible.
    • Extensibility: Supports custom operators, hooks, and executors.
    • Scalability: Can handle workflows with thousands of tasks.
    • Monitoring: Provides a rich UI for visualizing and monitoring workflows.
  4. Architecture:
    • Scheduler: Triggers tasks and schedules workflows based on dependencies and timing.
    • Executor: Handles the execution of tasks (e.g., LocalExecutor, CeleryExecutor).
    • Web Server: Provides a UI for managing and monitoring workflows.
    • Metadata Database: Stores metadata about DAGs, tasks, and their states.
  5. Use Cases:
    • ETL Pipelines: Extracting, transforming, and loading data between systems.
    • Data Orchestration: Coordinating tasks across multiple systems and services.
    • Machine Learning Pipelines: Automating the training and deployment of machine learning models.
    • DevOps Automation: Automating infrastructure provisioning and deployment tasks.
  6. Advantages:
    • Code-Based Workflows: Workflows are defined in Python, enabling version control and collaboration.
    • Rich UI: Provides a user-friendly interface for monitoring and troubleshooting workflows.
    • Community Support: Active community and extensive documentation.
  7. Challenges:
    • Learning Curve: Requires understanding of Python and workflow concepts.
    • Scalability Limits: May require additional configuration for very large-scale workflows.
    • Resource Intensive: Can consume significant resources for complex workflows.
  8. Ecosystem:
    • Providers: Packages that extend Airflow’s functionality (e.g., AWS, GCP, Azure integrations).
    • Plugins: Custom extensions for adding new features or integrations.
    • KubernetesExecutor: Allows running tasks in Kubernetes pods for better resource management.
  9. Best Practices:
    • Idempotent Tasks: Ensure tasks can be retried without side effects.
    • Modular DAGs: Break down large workflows into smaller, reusable DAGs.
    • Error Handling: Implement retries and alerts for task failures.