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
Observability designed for Spark data engineering
Not another data quality tool
Seamlessly scale
Single-point one-time installation.
Zero code changes.
Onboard each data source & asset.
Observe E2E
Any pipeline, any data.
Spark, DBT, SQL. On-prem, cloud.
Monitor data in warehouse.
Root-cause quickly
Full-stack visibility. Data+job lineage & code. Actionable recommendations.
Monitor data quality, no context.
Prevent in real-time
Detect & prevent incidents in-motion.
Avoid runs with invalid inputs.
Detect at-rest. Can't react.
Reduce effort
AI-generated & dynamic anomaly detection.
Manual data checks, static rules.
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