A financial services group had metrics in one tool, logs in another, and traces in a third, with no shared identifiers between them. AceMQ designed the consolidation onto a Grafana-fronted stack where an engineer can move from a metric spike to the relevant logs and traces without re-deriving context.
The three signal types used different names for the same things — service names differed by casing and suffix, environments were labeled inconsistently, and there was no shared trace or request identifier in the logs. Correlating an alert with its logs meant a human translating between naming schemes under time pressure. Duplicated agents across the estate also meant the same host was being scraped several times over.
Hybrid estate with Kubernetes workloads and legacy virtual machines, multiple regulated environments requiring data residency separation.
The design work started with a label taxonomy rather than tooling, because correlation is a naming problem before it is a product problem. We defined a required label set applied uniformly across metrics, logs, and traces, specified trace context propagation so log lines carry the trace identifier, and then designed the collection topology that enforces it at the agent layer.
Engineers now pivot from an alert to the correlated logs and trace directly in Grafana instead of manually translating identifiers. Removing duplicate collection cut telemetry volume noticeably, and the shared taxonomy has held as new services onboard.
Assessment of a sprawling Grafana dashboard estate to identify duplication, broken panels, and the small set of dashboards anyone actually uses.
Remediation of a Grafana deployment that became unusable during incidents, with dashboards timing out exactly when engineers needed them most.
Whether you need architecture advisory, 24/7 support, or full managed services, AceMQ has the expertise to help.