Apache Airflow
Apache Airflow Notes
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.