Datadog Jobs Monitoring
Spark & Databricks job optimization
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Detect Problematic Jobs
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.
Resolve Failures & Latency
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.
Cost-Optimize Clusters & Jobs
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
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.