Observe, troubleshoot, and cost-optimize your Spark and Databricks jobs across data pipelines

Data Jobs Monitoring (DJM) helps data platform teams and data engineers detect problematic Spark and Databricks jobs anywhere in their data pipelines, remediate failed and long-running jobs faster, and proactively optimize overprovisioned compute resources to reduce costs. Unlike traditional infrastructure monitoring tools, native interfaces, and log analysis, DJM is the only solution that enables teams to drill down into job execution traces at the Spark stage and task level to easily resolve issues and seamlessly correlate their job telemetry to their cloud infrastructure—in context with the rest of their data stack.

Features

• Real-Time Monitoring: Access real-time metrics to track the performance and health of data jobs.
• Advanced Analytics: Utilize AI-driven analytics to detect anomalies and optimize data job execution.
• Automatic Discovery: Automatically detect and monitor all data jobs in your infrastructure.
• Integration: Seamlessly integrate with various data sources and services for a unified monitoring experience.
• Custom Dashboards: Create personalized dashboards to visualize key metrics and monitor data job health at a glance.