Two events happening in the same second can be related. They can also be two unrelated requests on a busy service. Time is a useful starting filter, not a reliable join key.
I prefer an investigation path that follows the request where the instrumentation supports it: user-visible failure, delivery record, application operation, dependency call. Every hop should have an explicit explanation for how it is connected.
Start with stable service identity
Datadog recommends env, service, and version for correlating telemetry. Trace and span identifiers connect logs to an instrumented operation when they are correctly injected and parsed. Adding the same service label to everything does not automatically create distributed traces.
For AEM, confirm that the tracing and logging integration is supported in the particular deployment. If an edge request identifier is not propagated through the application, do not draw a line that the data cannot support.
Use an example with two possible causes
Imagine a page returning 500 while a downstream API is also slow. A timestamp search finds a timeout exception. That is a plausible lead. A trace containing the failing page operation and the timed-out dependency, with matching log context, is stronger evidence.
Even then, ask whether the dependency caused the failure or merely participated in it. Retries, cancellation, and shared resource exhaustion can make the first obvious span an incomplete explanation.
Plan for incomplete evidence
Logs and traces may be sampled independently. Datadog notes that a log can contain a trace identifier even when the corresponding trace was not ingested or retained. A missing trace is not automatically proof of broken correlation.
Write that limitation into the investigation guide. Provide a fallback using service, version, route family, time range, and a safe request identifier where available. Separate “we could not retain this trace” from “we have no evidence this request reached the application.”
Keep identifiers out of the wrong places
A request identifier can be useful in a log search without being a good metric tag. Putting every unique identifier into a metric dimension can produce an enormous number of series. Correlation design and aggregation design solve different problems.
The goal is a defensible chain of evidence. A dashboard should make following it easier, while being honest about the gaps.