Observe, fix, and optimize
Spark pipelines, in-motion
Monitor and control everything your data pipelines do.
In-motion, with zero code changes.
The definity platform
Full-Stack Spark-first data observability
Unified deep visibility across your platform – Spark, DBT, or anywhere. On-Prem or Cloud.
DEEP MONITORING
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
AI-POWERED COVERAGE
Shift to post-production
→ increase data coverage
Stop writing data checks manually
- Out-of-the-box coverage
- AI-generated tailored tests
- Dynamic anomaly detection
CONTEXTUALIZED RCA
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
PROACTIVE PROTECTION
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
SEAMLESS INSTRUMENTATION
Single-point one-time installation
→ zero code changes
Stop onboarding each new data source and asset
- Gain E2E observability in <30 minutes
Full-stack observability designed for Spark at-scale
Not another data quality tool
Seamless scalability
Single-point installation in <30 minutes.
Zero code changes.
Heavy onboarding of each asset.
Requiring engineers to add code.
Real-time prevention
In-motion detection & run-preemption.
Avoid runs with invalid inputs.
Late detection At-Rest.
Can't react, issues propagate.
Intelligent RCA
Full-stack visibility. Data+job lineage.
3-clicks RCA.
Only monitor Data Quality in DWH.
No context, high effort RCA.
Actionable optimizations
Pinpointed waste. High ROI opportunities.
Actionable recommendations.
No performance monitoring.
No optimization insights.
AI-Powered coverage
AI-generated & dynamic anomaly detection.
Proactive CI/CD validation.
Manual data checks. Static rules.
Long validation cycles.
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