Full-stack observability in runtime, with AI-powered insights – for Spark and Lakehouse pipelines












Monitor jobs and transformations inline, in run-time - not after-the-fact.
Check input data and job parameters before pipelines even run.
Catch data, job, or schema issues before SLAs are breached or bad output is written.
Stop problematic jobs mid-run to prevent wasted runs and downstream impact.



Monitor jobs execution, data quality, infra utilization, and cost across the entire stack.
Track SLAs, freshness, distribution, schema, code/env changes, and more.
Pinpoint issues out-of-the-box, without manual configurations or static rules.




Column-level lineage connecting datasets and transformations – for E2E data flow visibility.
Correlate runs, transformations, code, env, and data into a unified holistic view.
Go from alert to insight in seconds – no log-diving, just actionable insights.




Enterprise impact
prevented data incidents
increased data engineering velocity
standardized observability
faster deploys & upgrades
Instrument in <15 minutes with zero code changes – no developer action required.
Monitor data, jobs, and infra inline with execution and catch issues before they propagate.
Root-cause issues in 3-clicks with actionable context and AI assistance.
Instrument in <15 minutes and standardize observability across the platform in week-1
Central installation. Zero code changes. On-prem or cloud.

Learn more how definity enables data engineers to proactively monitor, detect, and prevent data & job issues, in real-time, out-of-the-box.
Monitor and debug pipelines in-motion, with full context and zero code changes.