Apache Airflow has helped us orchestrate complex data pipelines for hundreds of tables, maybe more than that, and maintain them easily.
Apache Airflow offers a stable platform for automating workflows, ideal for middle-scale tasks with its integration capabilities. It excels in task orchestration using DAGs and Python, enhancing automation. However, users face challenges with scalability, real-time ETL tasks, and frequent scheduling. Limited technical support and maintenance complexity, especially in Kubernetes, require manual intervention, making it less suited for extensive, real-time operations and frequent workflow updates.
