Modern production environments span Kubernetes, VMs, serverless, and event-driven systems, yet incident response still relies on manually stitching together dashboards, traces, and logs. While OpenTelemetry improves signal collection, it does not provide a consistent way to model dependencies across services, infrastructure, and asynchronous components. As a result, SREs spend valuable time correlating signals instead of identifying the root cause. This talk presents a graph-driven RCA approach built on a normalized observability layer that converts telemetry into relationships between services, infrastructure, and runtime events. Instead of treating traces, logs, and metrics independently, we derive a dependency graph enriched with time-based observations and health signals. This allows failures to be analyzed as propagation paths across systems rather than isolated alerts. We apply this approach to real-world scenarios across Kubernetes microservices, VM-based workloads, hybrid cloud environments, serverless platforms, and asynchronous systems such as Kafka. The graph highlights cascading failures, dependency latency, and hidden bottlenecks that are difficult to detect using traditional dashboards. We compare this workflow with trace-centric and metrics-first investigations, focusing on time-to-root-cause and operational effort during incidents. Attendees will learn how to build telemetry normalization layers, derive dependency graphs from observability data, and use graph-based reasoning to accelerate incident response. The session focuses on practical SRE workflows, design tradeoffs, and lessons learned from applying graph-driven RCA in distributed systems.
Founder of from-dev-to-ops Chennai based devops Community. Help Engineering teams implement Observability and Reliability Solutions.
