Apache Airflow Best Practices for Production

Production-Ready Airflow

Apache Airflow has become the de facto standard for orchestrating complex data workflows. However, running Airflow in production requires careful planning and adherence to best practices.

1. Use the KubernetesExecutor

For production environments, the KubernetesExecutor provides better resource isolation and scalability compared to the LocalExecutor or CeleryExecutor.

# Example Kubernetes configuration
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator

task = KubernetesPodOperator(
    task_id='example_task',
    name='example-pod',
    namespace='airflow',
    image='python:3.9',
    cmds=['python', '-c'],
    arguments=['print("Hello from Kubernetes!")'],
)

2. Implement Proper Monitoring

Set up comprehensive monitoring using tools like Prometheus and Grafana to track DAG execution times, failure rates, and resource utilization.

3. Version Control Your DAGs

Always version control your DAG files and use a CI/CD pipeline to deploy changes systematically.

Common Pitfalls to Avoid

• ❌ Don't put heavy computation in DAG files
• ❌ Avoid using dynamic DAG generation unless necessary
• ❌ Don't ignore task dependencies
• ❌ Never hardcode credentials

Conclusion

Following these best practices will help you build reliable, maintainable Airflow pipelines that scale with your organization's needs.

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