Monitor Apache Airflow with Datadog

Apache Airflow is an open source system for programmatically creating, scheduling, and monitoring complex workflows including data processing pipelines. It represents workflows as Directed Acyclic Graphs (DAGs), which are made up of tasks written in Python, allowing users to programmatically build and modify their workflows. The integration with Datadog provides key metrics like DAG duration and task status, enabling immediate insight into Airflow-managed workloads. To ensure optimal performance, users can track metrics such as average task duration, schedule delay, and concurrency limits, and use features like DogStatsD Mapper to surface slow tasks and DAGs. Additionally, the integration supports distributed tracing with Celery, providing visibility into workflow performance and helping identify latency issues. With this integration, Datadog offers comprehensive visibility into managed workflows, making it easier to meet service level agreements (SLAs) and optimize workflow execution.

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