Full-Stack Observability &
Cost Optimization for Databricks
Go beyond Databricks 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 Databricks?
Today
Limited Performance Monitoring
System Tables provide cost visibility but lack real-time monitoring of CPU & memory utilization, job-level inefficiencies, and performance trends.
Manual & At-Rest Data Quality
Lakehouse Monitoring requires manual setup for each job & table, and DQ checks run after the fact – leading to high effort, coverage gaps, and late detections.
Fragmented Execution Context
Unity Catalog tracks metadata, but there’s no single pane of glass for data, jobs, lineage, code, and env behavior – debugging is difficult across disparate tools.
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 on real data – to proactively avoid runaway costs, unexpected failures & SLA misses, and data integrity issues.
Contextualized Transformation-Level Observability
Deploy in Minutes – No Code Changes
1-click deployment via Databricks Init Script – covers all workloads
Fully secure – no data leaves your environment
Supports Databricks on AWS, GCP, and Azure
Migrating to Databricks?
Automate workloads validation
Accelerate Spark-to-Databricks 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
