Data Jobs Monitoring enables my organization to centralize our data workloads in one place. My team is able to resolve our Databricks job failures 20% faster because of how easy it is to set up real-time alerting and find the root cause of the failing job.

Matt Camilli

Head of Engineering at Rhythm Energy

Feature Overview

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.

Detect job failures and latency spikes anywhere in your data pipelines

Notify teams immediately when their jobs have failed or are still running beyond the expected completion time using out-of-the-box alerts
Visualize trends and anomalies in job performance to quickly analyze your data platform’s reliability and estimated costs
Prioritize job issue resolution more efficiently by using recommended filters to surface the important issues impacting job and cluster health, such as failures, latency, cost spikes, and more

Pinpoint and resolve failed and long-running jobs faster

Drill down into a detailed trace view of a job to see the full execution flow (i.e., job, stages, and tasks) and where it failed for full troubleshooting context
Uncover root cause of slow jobs by identifying inefficient Spark stages or SQL queries that could be impacted by data skew, disk spill, or other common factors
Compare recent runs of a job to expedite root cause analysis, surfacing trends and changes in run duration, Spark performance metrics, cluster utilization, and configuration

Reduce costs by optimizing misallocated clusters and inefficient jobs

Lower compute costs by identifying overprovisioned clusters and changing the number of worker nodes and instance types
Increase job run efficiency at the application level by using Spark execution metrics to determine improvements in the code or configuration
Surface the largest savings opportunities by viewing the idle compute for the largest jobs and cluster utilization over time, segmented by different data teams or environments to see which is incorrectly provisioned

Centralize data pipeline visibility with the rest of your cloud infrastructure

Gain complete data pipeline visibility in a unified dashboard, viewing data storage, warehouse, and orchestrator metrics from other key technologies such as Snowflake and Airflow in the same place as your job telemetry
Pivot seamlessly between key data pipeline metrics to understand what influenced job failures or latency spikes, such as infrastructure metrics, Spark metrics, logs, configuration
Accelerate incident response and debugging with flexible tagging that route alerts for data pipeline issues to the right teams