Databricks is an orchestration platform for Apache Spark that enables users to manage clusters and deploy Spark applications for highly performant data storage and processing. Datadog's Databricks integration unifies infrastructure metrics, logs, and Spark performance metrics, providing real-time visibility into the health of nodes and performance of jobs. By deploying Datadog to Databricks clusters, users can monitor job failures, optimize cluster configuration, and troubleshoot problems using detailed system metrics, logs, and Spark metrics. The integration allows for a complete view of Databricks clusters, including resource usage across specific clusters, and enables fine-tuning of Spark jobs for peak performance. Additionally, Datadog's integration provides visibility into shuffle operations, node size, and proper partitioning to minimize the impact on job runs. Users can also use logs to debug errors and correlate node exceptions with performance metrics. The integration is designed to provide real-time visibility into Databricks clusters, ensuring they are available, appropriately provisioned, and able to execute jobs efficiently.