Full-Stack Observability &
Cost Optimization for Dataproc
Go beyond GCP-native tools to ensure job health, data reliability, and cost efficiency –
in real-time, with no manual setup.
Why Do I Need Full-Stack Observability
for Dataproc?
Today
Limited Cost & Performance Monitoring
GCP Cloud Monitoring provides basic cluster resources aggregated, but lacks real-time job-level monitoring, anomaly detection, and optimization insights.
Lacking Out-of-the-Box Observability
No built-in data quality or execution health tracking. Ephemeral jobs lacking persistent logs are especially hard to monitor.
Fragmented RCA & Effort-Intensive Debugging
Troubleshooting is highly fragmented, manually piecing together (insufficient) info from Dataproc History Server, BigQuery Metadata, and Cloud Logging / Trace.
No Built-In CI Validation
Not testing pipeline code changes pre-deployment to detect cost spikes, runtime degradations, or data anomalies – risking production.
definity
AI-Powered Cost & Performance Optimization
Profile job-level performance, pinpoint CPU & Memory over-provisioning, detect degradations in real-time, and auto-tune jobs in 1-click – to immediately cut spend.
360°, Automated, Real-Time Observability
Monitor data quality, job execution, and performance – out-of-the-box, inline with job execution – to automatically detect anomalies in-motion.
Unified Intelligent RCA
Root-cause incidents in minutes with intelligent insights & unified transformation-level execution context, including deep data+job lineage and env tracking.
Seamless validation in CI
Automatically test code changes – preventing unexpected data outputs, SLA breaches, and runtime unpredictability due to scaling or configuration drift.
Contextualized Transformation-Level Observability
Deploy in Dataproc in Minutes – No Code Changes
1-click deployment via central Spark Submit – covers all workloads
Fully secure – deploy & run directly in your GCP VPC, no data leaves
Migrating to Dataproc?
Automate workloads validation
Accelerate Spark-to-Dataproc migration by months, with seamless workload validation on real data:
Ensure data reliability & pipeline stability, and easily pinpoint unexpected changes – in CI
Maintain job performance & prevent regressions
Avoid cost overruns & runtime surprises
