01
Live Dashboards
Configurable panels showing throughput, latency, error rates, and custom metrics in real time.
Observability & operationsGENEDATA / 01
See the signal.Understand the cause.
Follow pipeline health, data quality, and operational cost in context. Move from an alert to the workflow behind it.
Shared context · Lineage · Governance
Connected
Your sources
Pipeline events
Data quality
Resource usage
Connected intelligenceObservability
Governance
Business impactInvestigation & action
Shared contextLineageGovernance
02
Smart Alerting
Threshold-based and ML-powered anomaly alerts delivered via Slack, PagerDuty, or any webhook endpoint.
03
Historical Replay
Replay any past time window with full fidelity to diagnose incidents and validate fixes before redeployment.
Anomaly Detection
Deviation from the learned baseline, not a static threshold.
Static thresholds are wrong twice: they fire on normal seasonal peaks and stay silent through a slow degradation. The engine learns each signal's expected range from its own history, so an alert means genuinely unusual rather than merely large.
How It Works
Instrument, baseline, detect, replay.
Signals are emitted by the engine as work executes, so coverage does not depend on anyone remembering to instrument a new pipeline.
Instrument automatically
Freshness, volume, schema, latency, and error signals are emitted for every table and task without manual instrumentation.
Learn the baseline
Each signal's normal range is learned from its own history, including weekday, month-end, and seasonal shape.
Detect and route
Deviations raise alerts enriched with lineage, so the notification names both the failing task and everything downstream.
Replay the window
Reconstruct any past interval at full fidelity to confirm a root cause and validate a fix before redeploying it.
Capabilities
The signals that actually predict incidents.
Most data incidents are freshness, volume, or schema problems noticed too late. These are watched by default.
Freshness monitoring
Every table carries an expected update cadence; a late arrival alerts before a dashboard silently serves yesterday.
Volume anomalies
Row counts compared against learned seasonality, catching partial loads that a row-count-greater-than-zero check would pass.
Distribution drift
Null rates, cardinality, and value distributions tracked per column to catch upstream changes that do not break the schema.
Lineage-aware alerting
An alert names every downstream dashboard, model, and report affected, so triage starts with known impact.
Deduplicated routing
One upstream failure produces one incident rather than forty alerts from every dependant table.
Full-fidelity replay
Thirteen months of retention at one-second resolution, so post-incident review works on data rather than recollection.
In Practice
Who this is for.
Observability pays off in the minutes between something breaking and someone noticing.
Platform / SRE
Cut the alert storm to one incident
Lineage-aware deduplication collapses a cascade of downstream failures into a single incident pointing at the actual cause.
Triage starts at the root, not the symptom.
Data Engineer
Catch partial loads before publication
Volume and distribution checks run on the pipeline, so a half-loaded partition fails the gate instead of reaching the warehouse.
Bad data stopped upstream of every consumer.
Data Analyst
Know whether a number is trustworthy
Every dashboard shows the freshness and health of the tables behind it, so a stale figure is visible rather than assumed current.
Confidence stated on the dashboard itself.
What Changes
What changes with signals from the engine.
Bolt-on monitoring can only observe what it was configured to look at, which is never the thing that breaks.
| Dimension | Before Genedata | With Genedata |
|---|---|---|
| Coverage | Only the pipelines someone instrumented | Every table and task, emitted automatically |
| Thresholds | Static limits that misfire on seasonality | Learned baselines per signal |
| Detection point | A stakeholder notices a wrong number | The failing task alerts before publication |
| Alert volume | One failure produces dozens of alerts | Deduplicated into a single lineage-aware incident |
| Post-incident review | Reconstructed from partial logs and memory | Full-fidelity replay of the exact window |
Freshness · Volume · SchemaChecks
7 yearsAudit Retention
Slack · PagerDuty · WebhookAlert Channels
Learned baselineDetection
The next step
See everything. Fix anything. Instantly.
The observability layer gives your team complete situational awareness across every pipeline, service, and data stream in your stack — with lineage attached, so an alert tells you what is downstream as well as what broke.
FAQ
Observability, answered.
What platform and data teams ask about catching problems before users do.
What is monitored by default?
Freshness, volume, schema, latency, and error signals are emitted for every table and task automatically. Coverage does not depend on anyone remembering to instrument a new pipeline, which is exactly where bolt-on monitoring fails.
Why learned baselines instead of thresholds?
Static thresholds are wrong twice: they fire on normal seasonal peaks and stay silent through a slow degradation. Each signal's expected range is learned from its own history, including weekday, month-end, and seasonal shape.
How is alert fatigue avoided?
Alerts are deduplicated using lineage, so one upstream failure produces a single incident naming the root cause rather than forty alerts from every dependant table.
Can we reconstruct what happened during an incident?
Yes. Thirteen months of retention at one-second resolution means post-incident review works on data rather than recollection, and a fix can be validated against the exact window that broke.
Does this replace our APM?
No. This is data observability — the health of tables and pipelines — rather than application performance monitoring. Alerts route to Slack, PagerDuty, or any webhook, so they land alongside your existing operational tooling.
Related content
Guide
Signal Reference
Every emitted signal, what it detects, and how baselines are learned.
RunbookIncident Triage
Working from a lineage-aware alert to a root cause.
GuideData Contracts
Assertions that halt publication instead of alerting after it.
StatusPlatform Status
Live health and incident history across every region.
Take the next step
See a problem before your users do.
Watch a volume anomaly surface, route, and resolve with lineage attached — or start with the signal reference.