Easily optimize at-scale with dynamic auto-tuning and deep code optimizations









Monitor performance, SLAs, code, and cost at the job and query levels, with insightful drill-downs.
Automatically identify degradations in-motion, before they impact downstream.
Optimize jobs dynamically across clusters, jobs, and code.
Identify and fix CPU and memory utilization, Data Skew, Spill, Shuffle/Partitions, and more.
Avoid getting lost in Spark UI, with a unified context linking performance, data behavior, and lineage.
Monitor all Spark pipelines, on-prem or cloud, with zero code changes.
Pinpoint waste and job-specific savings opportunities, out-of-the-box.
Auto-tune jobs at-scale, to free up cluster resources, cut costs, and reduce job failures and run-times.
Fix inefficient code with tailored code recommendations based on job-specific performance and data behavior.
Enterprise impact
cost saving
fewer SLA misses
auto-tuning
faster deploys & upgrades
Track performance, inefficiencies, and cost at job-level, and detect degradations in real-time.
Profile job and data behavior, and identify optimization opportunities with high ROI
Auto-tune jobs dynamically and deeply optimize code with actionable recommendations.
Instrument in <15 minutes and start capturing savings within week-1
Central installation. Zero code changes. Cloud or on-prem.
Learn more how definity enables data engineers to cut Spark costs and ensure SLAs with agentic auto-tuning and deep code optimizations.
Optimize your Spark jobs in minutes and slash operational costs