Apache Airflow Advanced
Apache Airflow is the industry standard for orchestrating complex data pipelines. At the advanced level, you move beyond basic DAGs into dynamic composition, scalable architectures (Kubernetes, distributed executors), monitoring, and productionization. The difference between a mid-level and senior Airflow engineer is the ability to design resilient, self-healing workflows that handle millions of tasks per day. Advanced mastery unlocks $120k-180k roles in data engineering and ML ops.
什么是 Apache Airflow Advanced
Apache Airflow is a workflow orchestration platform that schedules, monitors, and executes complex data pipelines. At the advanced level, you design and operate Airflow at production scale, managing thousands of concurrent tasks, ensuring fault tolerance, integrating with Kubernetes, and monitoring SLAs. Airflow separates the orchestration layer (scheduling, retries, parallelism) from the task layer (Python, SQL, APIs). Advanced practitioners write idempotent, self-documenting DAGs, optimize database performance, and build deployment pipelines that enable rapid iteration without downtime.
💰 各地区薪资
| 地区 | 初级 | 中级 | 高级 |
|---|---|---|---|
| USA | $100k | $150k | $220k |
| UK | £70k | £110k | £160k |
| EU | €75k | €115k | €170k |
| CANADA | C$110k | C$160k | C$240k |
🎓 认证
🎯 使用 Apache Airflow Advanced 的职业
❓ 常见问题
What's the difference between Airflow mid-level and advanced?
Should I use KubernetesPodOperator or CeleryExecutor in 2026?
How do I handle dynamic DAG generation?
What monitoring tools pair with Airflow?
How do I test DAGs?
Is Airflow still relevant with dbt Cloud + dbt-orchestration?
What's the hardest part of scaling Airflow to millions of tasks?
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