observability

Collect, filter, and forward Docker container logs to Elasticsearch, S3, or stdout using Fluentd as a logging driver or sidecar.

Build interactive Grafana dashboards that visualize Prometheus metrics with panels, variables, and alerts for thorough service observability

Instrument applications and infrastructure with Prometheus metrics, configure alerting rules, and set up recording rules for efficient monitoring of service health

Build a custom Prometheus metrics exporter in Python using prometheus_client for counters, gauges, histograms, and summaries.

Trace requests across distributed microservices with OpenTelemetry, Jaeger, and Zipkin for latency debugging and performance optimization.

How to implement custom health indicators with Spring Boot Actuator, including database, Redis, external API checks, and Kubernetes readiness probes.

How to expose custom application metrics using Micrometer and Prometheus in Spring Boot, including counters, gauges, timers, and histograms.

Centralize logs from distributed services with ELK, Fluentd, and Loki for search, alerting, and troubleshooting in production.

Collect, aggregate, and expose application and infrastructure metrics with Prometheus, StatsD, and OpenTelemetry for monitoring and alerting.

How to use pino for fast structured JSON logging in Node.js, including log levels, child loggers, transports, and integration with Express and Fastify.

How to integrate Sentry for error tracking in Node.js Express applications, including error handlers, performance monitoring, release tracking, and source maps.

How to configure daily log rotation in Node.js using winston and winston-daily-rotate-file, including size limits, retention, compression, and transport combining.

Monitor API performance and health with Prometheus metrics, custom collectors, and alerting rules.

How to implement distributed tracing in Python with OpenTelemetry SDK, including spans, context propagation, auto-instrumentation, and Jaeger export.

How to expose custom business metrics in Python using prometheus_client, including counters, gauges, histograms, summaries, and Flask integration.

How to emit structured JSON logs in Python using structlog, including context binding, log levels, processors, and integration with standard logging.

Monitor actual user experiences with Core Web Vitals, session replay, and performance analytics to identify real-world bottlenecks.

Implement structured logging with JSON output, correlation IDs, and log aggregation for production observability.

Expose lightweight health endpoints so orchestrators, load balancers, and monitoring tools can verify service availability.

How to expose circuit breaker state as metrics for observability. Covers Prometheus integration, alerting rules, dashboards, and state transitions.

How to propagate correlation IDs across service boundaries for end-to-end request tracing. Covers HTTP headers, message queues, and logging integration.

How to propagate trace context across service boundaries with OpenTelemetry. Covers span creation, context propagation, sampling, and trace analysis.

How to implement liveness and readiness probes for container orchestration. Covers Kubernetes probes, dependency checks, graceful degradation, and probe endpoints.

How to collect, tag, and aggregate business metrics for observability. Covers Prometheus, OpenTelemetry, custom metrics, histograms, and dashboarding.

How to emit structured JSON logs with consistent fields for searchability. Covers Python structlog, Winston, Serilog, log levels, and log aggregation.

A document template for defining structured logging conventions, log levels, retention, and observability requirements across services.

A policy template that defines how alerts are configured, routed, escalated, and reviewed across services and infrastructure.

A blameless postmortem template for analyzing incidents, identifying root causes, and documenting lessons to prevent recurrence.

A template for defining reliability targets, error budgets, and measurement methods for services and systems.

Build a production monitoring stack. Covers Prometheus, Grafana, AlertManager, metrics instrumentation, alert rules, runbooks, SLI/SLO/SLA, distributed tracing with Jaeger, log aggregation, and on-call best practices with practical configuration examples.

A practical guide to observability: the three pillars (metrics, logs, traces), implementing with Prometheus, Grafana, Loki, Tempo/Jaeger, and building SLO-driven alerting.

A practical guide to OpenTelemetry: instrumentation, collectors, exporters, and wiring OTLP to backends like Jaeger, Prometheus, and Grafana.

A practical guide to SRE: defining SLIs, SLOs, and SLAs, managing error budgets, toil reduction, on-call rotations, and building a culture of reliability.

Run Apache Kafka in production with confidence. Covers partitions, replication, consumer groups, monitoring, performance tuning, and operational best practices for high-throughput streaming pipelines.

A practical guide to alert management: reducing alert fatigue, defining severity levels, escalation policies, on-call rotation design, and building a sustainable alerting culture.

Master distributed tracing with OpenTelemetry, Jaeger, and Zipkin. Trace propagation across services, span context, sampling strategies, and production debugging.

Set up metrics, logs, and traces with Grafana, Prometheus, Loki, and Tempo. Covers instrumentation, dashboards, alerting, and distributed tracing for production systems.

Master Prometheus metrics collection and Grafana dashboards. Covers metric types, PromQL, service instrumentation, alerting rules, and production deployment patterns.

Master Sentry for production error tracking. Covers SDK integration in Python, Node.js, Java, release tracking, source maps, performance monitoring, and alerting.

Master structured logging with JSON format, correlation IDs, log levels, and aggregation. Covers Python structlog, Node.js pino, Java SLF4J, ELK and Loki stacks.

A practical guide to distributed tracing: instrumenting applications, trace propagation, sampling strategies, and diagnosing latency in microservice architectures with OpenTelemetry, Jaeger, and Zipkin.

A practical guide to incident response: declaring incidents, building an incident command structure, communication protocols, and reducing mean time to resolution with structured processes.

A practical guide to log aggregation: structured logging, shipping strategies, retention policies, and building searchable log pipelines with ELK, Loki, and cloud-native solutions.

A practical guide to metrics and dashboards: instrumenting applications, choosing metric types, building useful dashboards, and creating alerting pipelines with Prometheus, Grafana, and Datadog.

A practical guide to conducting blameless postmortems: capturing timelines, identifying root causes, writing useful follow-ups, and building a culture of continuous improvement from outages.

Run AWS Lambda in production with confidence. Covers cold start optimization, layers, deployment patterns, observability with X-Ray, security hardening, connection pooling, and cost tuning for production workloads.