Full-Stack Observability &
Cost Optimization for EMR
Go beyond AWS-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 EMR?
Today
Limited Cost & Performance Monitoring
AWS CloudWatch 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.
High Effort Debugging
Troubleshooting is highly fragmented, manually piecing together insufficient info from Step Execution logs, CloudTrail, CloudWatch, and Glue metadata.
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 underutilization, detect inefficiencies 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 EMR in Minutes – No Code Changes
1-click deployment via central Spark Submit – covers all workloads
Fully secure – deploy & run directly in your AWS VPC, no data leaves
Migrating to EMR?
Automate workloads validation
Accelerate Spark-to-EMR 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
