Airflow is a popular open source platform that enables users to author, schedule, and monitor workflows programmatically. It helps teams run complex pipelines by providing task orchestration, dependency management, and efficient scheduling across many different tools. Airflow is particularly useful for creating data processing pipelines, orchestrating machine learning training, and running cloud services. The platform is widely used in data processing use cases, with 85.5% of users relying on it for ETL/ELT pipelines. By defining workflows as Python code, teams can manage them more efficiently, automate creation, scheduling, and provisioning, and make them parameterizable and extensible. However, running workflows in production requires monitoring potential problems such as task failures and latency, task throttling, orphaned tasks, and more. Airflow's scheduler uses Directed Acyclic Graphs (DAGs) to define workflows, which can be parsed by the scheduler to schedule and run defined tasks. The scheduler is managed by a pool of workers that execute tasks, with the triggerer helping to defer worker slots to run new tasks while queued tasks are waiting to execute. Airflow offers a suite of out-of-the-box metrics that can be collected using StatsD to track key performance indicators such as task failures, operator failures, DAG run duration, schedule delay, number of queued tasks, and more. By monitoring these metrics, teams can identify potential issues, optimize their workflows, and ensure the reliability and performance of their Airflow deployments.