Data Jobs Monitoring detects and helps resolve job failures and latency spikes across data pipelines
NEW YORKÂ — Datadog, Inc. (NASDAQ: DDOG), the monitoring and security platform for cloud applications, today announced the general availability of Data Jobs Monitoring, a new product that 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 optimize overprovisioned compute resources to reduce costs.
Data Jobs Monitoring immediately surfaces specific jobs that need optimization and reliability improvements. It also enables 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 Matt Camilli, Head of Engineering at Rhythm Energy. “As a result, 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.”
“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 Michael Whetten, VP of Product at Datadog. “Data Jobs Monitoring helps teams do just that by giving data platform engineers full visibility into their largest, most expensive jobs to help them improve data quality, optimize their pipelines and prioritize cost savings.”
Data Jobs Monitoring helps teams to:
- 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 they negatively impact the end user experience. Recommended filters surface the most important issues 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, giving them 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/.
About Datadog
Datadog is the observability and security platform for cloud applications. Our SaaS platform integrates and automates infrastructure monitoring, application performance monitoring, log management, user experience monitoring, cloud security and many other capabilities to provide unified, real-time observability and security for our customers’ entire technology stack. Datadog is used by organizations of all sizes and across a wide range of industries to enable digital transformation and cloud migration, drive collaboration among development, operations, security and business teams, accelerate time to market for applications, reduce time to problem resolution, secure applications and infrastructure, understand user behavior and track key business metrics.
Forward-Looking Statements
This press release may include certain “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended including statements on the benefits of new products and features. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control, including those risks detailed under the caption “Risk Factors” and elsewhere in our Securities and Exchange Commission filings and reports, including the Quarterly Report on Form 10-Q filed with the Securities and Exchange Commission on November 7, 2023, as well as future filings and reports by us. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations, or otherwise.
Contact
Dan Haggerty
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SOURCE Datadog, Inc.
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