Modern data engineering revolves around automation, reliability, and scalability. Writing an ETL script in Python is only the beginning. To transform that script into a production-grade data pipeline, you need orchestration, scheduling, monitoring, and error handling. This is where Apache Airflow shines. Apache Airflow is one of the most popular workflow orchestration tools in data engineering. It allows you to define, schedule, and monitor workflows programmatically using Python. Instead of manually running your ETL scripts, Airflow automates the entire process and ensures your data pipelines execute reliably. Why Apache Airflow Matters After developing an ETL pipeline in Python, several challenges remain: • How do you schedule it to run automatically? • How do you monitor failures? • How do you retry failed tasks? • How do you manage dependencies? • How do you scale multiple workflows? Apache Airflow solves all these problems by acting as the orchestrator for your ETL workflows.…