Observe, fix, and optimize
pipelines, in-motion

Monitor and control everything your data pipelines do.
In-motion, with zero code changes.

The definity platform

Pipeline Observability

Monitor data and pipelines in-motion to proactively prevent downtime and quickly root-cause issues

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Performance Optimization

Optimize pipeline runs and jobs performance to save costs and keep SLAs

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Lifecycle Management

Accelerate code deployments and platform upgrades while maintaining reliability and performance

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Spark-first data pipeline observability

Unified deep visibility across your platform – Spark, DBT, or anywhere. On-Prem or Cloud.


Monitor data & pipelines
→ maintain platform reliability

Stop guessing how your data operates

  • Data quality – volume, freshness, distribution, schema
  • Pipeline reliability – runs, SLAs, performance
  • Platform health – env, configuration, versions


Shift to post-production
→ increase data coverage

Stop writing data checks manually

  • Out-of-the-box coverage
  • AI-generated tailored tests
  • Dynamic anomaly detection


Understand the context
→ root-cause issues quickly

Stop pulling teeth to root-cause breakages

  • E2E column-level data+job lineage
  • Code & environment changes analysis
  • Actionable pinpointed alerts


Detect issues in-motion
→ mitigate in real-time

Stop catching data issues too late

  • Data & performance checks inline with pipeline runs
  • Checks on input data, before pipelines even run
  • Automatic preemption of runs


Single-point one-time installation

→ zero code changes

Stop onboarding each new data source and asset

  • Gain E2E observability in <30 minutes

Shift observability to post-production

Let data developers focus on business value

Prevent data downtime

  • Increase data & pipeline coverage
  • Minimize Time to Detect

Prevent data downtime

Increase developers velocity

  • Reduce Time to Resolve
  • Eliminate manual test writing

Increase developers velocity

Reduce infrastructure cost

  • Optimize resource utilization
  • Minimize re-runs & orchestration bottlenecks

Reduce infrastructure cost

Regain trust in data

  • Understand data coverage & health
  • Restore data team’s reputation

Regain trust in data

Establish engineering standards

  • Increase consistency and accountability
  • Enforce standards

Establish engineering standards