Data Jobs Monitoring detects and helps resolve job failures and latency spikes across data pipelines
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Data Jobs Monitoring immediately surfaces specific jobs that need optimization and reliability improvements while enabling teams to drill down into job execution traces so that they can correlate their job telemetry to their cloud infrastructure for fast debugging.
“Data Jobs Monitoring enables my organization to centralize our data workloads in a single place—with the rest of our applications and infrastructure—which has dramatically improved our confidence in the platform we are scaling,” said
“When data pipelines fail, data quality is impacted, which can hurt stakeholder trust and slow down decision making. Long-running jobs can lead to spikes in cost, making it critical for teams to understand how to provision the optimal resources,” said
Data Jobs Monitoring helps teams to:
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Detect job failures and latency spikes: Out-of-the-box alerts immediately notify teams when jobs have failed or are running beyond automatically detected baselines so that they can be addressed before there are negative impacts to the end user experience. Recommended filters surface the most important issues that are impacting job and cluster health, so that they can be prioritized. -
Pinpoint and resolve erroneous jobs faster: Detailed trace views show teams exactly where a job failed in its execution flow so they have the full context for faster troubleshooting. Multiple job runs can be compared to one another to expedite root cause analysis and identify trends and changes in run duration, Spark performance metrics, cluster utilization and configuration. -
Identify opportunities for cost savings: Resource utilization and Spark application metrics help teams identify ways to lower compute costs for overprovisioned clusters and optimize inefficient job runs.
Data Jobs Monitoring is now generally available. To learn more, please visit: https://datadoghq.com/product/data-jobs-monitoring/.
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