Monitor Databricks with Datadog
Datadog, Friday, October 2nd, 2026
Datadog shows how its Databricks integration monitors jobs, clusters, data quality, Model Serving endpoints and costs in one place.
The final post in Datadog's Databricks series explains how its integration brings Databricks telemetry into Datadog alongside the rest of a data pipeline.
It uses three collection paths: the Datadog Agent on classic compute clusters for real-time Spark metrics and logs, Databricks API polling for job and task run data, and system tables.
Users can optimize jobs and clusters, catch data quality issues with Data Observability, monitor Model Serving endpoints and attribute costs with Cloud Cost Management.