Full-stack observability in-motion, with actionable lineage and context – for Spark and Lakehouse pipelines
See how definity helps you detect pipeline issues in real time and resolve them in minutes with full context and lineage.
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 UI.
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, lineage, and insights.
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 &Â job issues, in real-time, out-of-the-box.
Monitor and debug pipelines in-motion, with full context and zero code changes.