Observe, fix, and optimize
Lakehouse & Spark pipelines, in-motion
Monitor and control everything your data pipelines do.
In-motion, with zero code changes.
The definity platform
Full-Stack data observability, in-motion
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
Proactive observability designed for the Lakehouse & Spark
Not another data quality tool
Seamless scalability
Central 15 minutes installation.
Zero code changes.
Heavy onboarding & dev effort.
Real-time prevention
In-motion detection & preemption, even before pipeline starts.
Late detection. Issues propagate.
Intelligent RCA
Full context & lineage.
3-click RCA.
DQ only. High effort RCA.
Easy optimizations
Job-level recommendations
1-click auto-tuning.
Long & manual investigations.
AI-Powered coverage
AI-powered anomaly detection & CI/CD validation.
Manual checks. Long validations.
Stop firefighting. Standardize proactive observability.
Let data developers focus on business value
30% infra cost savings
- Optimize resource utilization at job-level
- Proactively detect degradations
30% infra cost savings
90% prevented data incidents
- Scale data & pipeline coverage to 100%
- Detect immediatelt - zero time to detect
90% prevented data incidents
25% increased dev velocity
- Minimize time to root-cause & tune jobs
- Eliminate manual checks & validation
25% increased dev velocity
50% faster deployments
- Accelerate platform upgrades & migrations
- De-risk ongoing pipeline code-changes
50% faster deployments
Regain trust in data
- Ensure data quality, job SLA, platform health
- Restore data team’s reputation
Regain trust in data
Establish eng standards
- Increase consistency and accountability
- Dynamically enforce standards & contracts