Agentic Optimization & Observability for Databricks
Optimize costs across Classic Compute and Serverless, protect SLAs, and improve data reliability – with zero-touch.
Why Do I Need Agentic Optimization
and 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 Optimization
Agentically auto-tune jobs, route jobs to optimal compute type (Classic or Serverless), and easily optimize inefficient code
360°, Automated, Real-Time Observability
Monitor data quality, job health, and performance out-of-the-box, and prevent incidents in-motion
Unified Intelligent Troubleshooting
Actionable explainability, deep lineage, and agentic RCA across the Databricks platform – to resolve 10x faster
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 and Service Principal
Covers all workloads – across Classic Compute and Serverless
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
