Observe, fix, and optimize
Lakehouse & Spark pipelines — in-motion
Monitor data, jobs, and performance in real time – with zero code changes.
Proactively cut costs, prevent incidents,
and troubleshoot issues
Cost & Performance Optimization
Cut platform costs and ensure SLAs with job-level optimizations and auto-tuning.
Pinpoint CPU & memory overprovisioning, job inefficiencies, and run time bottlenecks.
Profile job execution over time, detect degradations, and identify savings opportunities.
Easily optimize with job-specific recommendations and 1-click auto-tuning.
In-Motion Observability
Prevent incidents in real-time with inline job monitoring and full-stack coverage.
Monitor your entire data stack in one place - data, jobs, usage, infra, and cost.
Detect anomalies out-of-the-box with AI-powered detection.
Prevent issues in real-time with automated run preemptions.
Actionable RCA
Simplify troubleshooting with actionable lineage, context, and insights.
Automated column-level lineage connecting datasets, jobs, and transformations.
Contextualized job execution with transformation, code, and env tracking.
A single pane of glass for actionable alerts, intuitive UI, AI-powered insights.
CI/CD Testing
Accelerate platform upgrades and pipeline code-changes with seamless validation in CI.
Automatically simulate runs in staging without re-routing data or manual setup.
Catch data issues and performance regressions before they reach production.
Pinpoint issues to specific changes in code, inputs, or environment.
Seamless Instrumentation
Central one-time installation with zero code changes.
Scale observability E2E across all workloads, in <15 minutes
Secure by design - runs 100% in your environment. Data never leaves.
Proven Results, Fast
Works Seamlessly Across Your Lakehouse Stack
Deploy anywhere – cloud, on-prem, on kubernetes, or hybrid.
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
Infra cost savings
- Optimize resource utilization at job-level
- Proactively detect degradations
Infra cost savings
Prevented data incidents
- Scale data & pipeline coverage to 100%
- Detect immediately - zero time to detect
Prevented data incidents
Increased dev velocity
- Minimize time to root-cause & tune jobs
- Eliminate manual checks & validation
Increased dev velocity
Faster deployments
- Accelerate platform upgrades & migrations
- De-risk ongoing pipeline code-changes
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