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Explains when to capture dumps, what to compare, and how to use roots without turning dumps into guesswork.
Explains one observation lifecycle feeding metrics, tracing, and context through registered handlers.
Builds stable Micrometer dimensions that answer operational questions without unbounded time series and cost.
Propagates trace and observation context through supported clients and executor boundaries without passing framework state manually.
Covers allocation rate, collection frequency, latency symptoms, and safe production steps for GC-heavy services.
Explains health endpoints, readiness versus liveness, warm-up, dependency checks, and safe App Service health behavior.
Creates queryable structured events with trace correlation, stable fields, useful context, and deliberate redaction.
Moves from user-visible latency to route, dependency, database, executor, JVM, and change evidence before selecting a fix.
Gives a practical incident flow for memory growth using metrics, traffic correlation, heap evidence, and safe mitigation.
Explains correlation IDs, distributed tracing, dependency telemetry, message boundaries, and logs across App Service, queues, and downstream APIs.
Uses thread dumps, heap dumps, logger changes, mappings, and configuration evidence through a tightly controlled operator path.
Uses Actuator for operational visibility while controlling endpoint exposure, access, detail, and network reachability.
Builds a bounded readiness check for one required dependency while keeping liveness independent and responses non-sensitive.
Explains how to inspect EF Core generated SQL and connect LINQ shape to database execution behavior.
Investigates a growing-data EF Core slowdown through one correlated request, generated SQL, representative parameters, execution plan, waits, query shape, measured repair, and guarded rollout.
Explains how to triage post-release 500 errors using deployment correlation, logs, traces, feature flags, and rollback safety.
Explains how to diagnose 401/403 spikes across token validation, identity provider issues, clock skew, deployment changes, and abuse.
Explains when to store large payloads outside the broker and pass references through messages.
Explains cache observability across hit rate, latency, source load, memory, evictions, stale data, and fallback behavior.
Chooses between a modular monolith and microservices using domain boundaries, team ownership, scaling, deployment, data consistency, and operational cost.
Explains operational signals for queue-backed systems and how to respond when consumers fall behind.
Explains when backend work belongs in a queue instead of slow or unreliable inline request handling.
Compares OpenTelemetry-based Azure Monitor instrumentation with older Application Insights SDK patterns for .NET services.
Preserves order only within the business key that requires it, while handling hot keys, gaps, stale events, replay, and poison messages explicitly.