Event correlation and noise reduction for a distributed platform

Challenge

A typical situation: dozens of services each emit their own alerts, so one underlying failure produces a flood of pages across teams. Engineers spend the first part of every incident manually correlating symptoms.

Architecture

• Unified telemetry pipeline with OpenTelemetry and a central event stream
• Correlation rules that group alerts, deployments and topology into single incidents
• Anomaly detection baselines for key service metrics
• Incident views linking correlated events to recent changes and runbooks

Approach

• Fix telemetry quality and service catalog metadata first
• Map the most common incident patterns and their alert signatures
• Introduce correlation and suppression in observation mode
• Enable noise reduction gradually, measuring missed incidents

Technology

OpenTelemetry · Kafka · Prometheus · Grafana

Implementation

• Standardize labels and trace context across services
• Build correlation rules per incident pattern
• Route correlated incidents with full context to the owning team
• Tune anomaly detection against real workload seasonality

Intended outcome

The intended result is fewer, richer incidents: one page with context instead of a storm of alerts, and faster diagnosis because recent changes and likely causes are surfaced automatically.

Lessons learned

• AIOps amplifies the data it is given — telemetry quality comes first
• Run new rules in observation mode before suppressing anything
• Correlation rules need owners, just like the services they watch