Observability Studio
Where Performance Meets Observability
Learn how to connect p95/p99, traces, and saturation signals into a repeatable RCA workflow—so slowdowns become explainable.
Tool-agnostic. Hands-on. Focused on evidence.
Why observability?
Most performance work gets stuck at the symptom: “it’s slow.”
Observability is the missing bridge from symptoms to evidence:
where the time goes, what changed,
and why the system behaves differently under load.
Focus topics: tail latency (p95/p99) • tracing-first RCA • saturation • pool/queue exhaustion • retries/timeouts • JVM + Kubernetes signals
What you’ll find here
Short, practical content designed to help you diagnose faster — whether you use Instana, Dynatrace, Datadog, Grafana, or OpenTelemetry. The UI changes, but the method doesn’t.
Repeatable RCA checklists for common latency & reliability failures.
Hands-on experiments (e.g., OpenTelemetry demo) to practice evidence-based debugging.
Concise explainers: p99, RED/Golden Signals, sampling, context propagation.
Vendor-neutral workflows, strengths, trade-offs, and pricing drivers.
Start here (learning path)
If you’re new to observability: don’t start with tools. Start with questions and evidence.
Here’s a simple path from “what is this?” to “I can do RCA under load.”
- APM vs Observability
— what changes when you go from symptoms to evidence - Traces, Metrics, Logs
— what each signal is good for (and where it lies) - p95 vs p99 (tail latency)
— why performance breaks at the tail first - Root Cause Analysis Checklist
— a repeatable 60–90 min workflow - Kubernetes signals for RCA
— CPU throttling, memory pressure, restarts
New here? Go to the full Start Here page →