DevOps Training

  If you build or operate production systems, this article is a practical, engineer-friendly guide to the  reliability patterns that keep services alive under real-world failures —with clear explanations of  retries, timeouts, circuit breakers, and bulkheads , plus how to apply them without causing retry storms, cascading failures, or hidden latency spikes. Most outages don’t start as “big failures.” They start as small slowdowns that cascade. These patterns help you stop the cascade: ✅  Retries  → only when safe (use backoff + jitter, retry budgets, and idempotency) ✅  Timeouts  → set strict limits (no infinite waits; align client/server timeouts) ✅  Circuit Breakers  → fail fast when dependencies degrade (protect latency + threads) ✅  Bulkheads  → isolate blast radius (separate pools/queues per dependency or tier) Read here: https://www.cloudopsnow.in/reliability-patterns-that-keep-systems-alive-retries-timeouts-circuit-breakers-b...

  If you’re an engineer who’s tired of scaling “by gut feel,” this article is an engineer-friendly playbook for  cloud capacity planning —how to translate  CPU, memory, QPS, latency, and scaling limits  into real decisions (what to scale, when to scale, and how to avoid overprovisioning while still protecting performance). Capacity planning isn’t just “add more nodes.” It’s a repeatable loop: ✅  Measure  → baseline CPU/memory, QPS, p95/p99 latency, saturation signals ✅  Model  → understand bottlenecks, set SLO-based headroom, identify constraints (DB, cache, network, limits) ✅  Scale  → right autoscaling strategy (HPA/VPA/Cluster Autoscaler/Karpenter), safe thresholds, load tests ✅  Operate  → dashboards + alerts + regular review so growth doesn’t become incidents Read here: https://www.cloudopsnow.in/capacity-planning-in-cloud-cpu-memory-qps-latency-scaling-the-engineer-friendly-playbook/ #CapacityPlanning #Cloud #PerformanceE...

   If you’re dealing with constant Slack/PagerDuty pings and “alert storms,” this guide is a practical, engineer-friendly playbook to reduce noise and improve incident response by focusing on actionable alerts using routing, deduplication, and suppression—the same core techniques recommended across modern observability practices to prevent alert fatigue and missed real incidents. (Datadog) Alert fatigue isn’t a “people problem” — it’s a signal design problem. Fix it with a simple operating model: ✅ Route alerts to the right owner/on-call (service/team/env-aware) ✅ Dedup repeated notifications into a single incident (group + correlate) ✅ Suppress noise during known conditions (maintenance windows, downstream cascades, flapping) ✅ Escalate only when it’s truly actionable and time-sensitive Read here: https://lnkd.in/g4apHtec #AlertFatigue #SRE #DevOps #Observability #IncidentManagement #PagerDuty #OnCall #ReliabilityEngineering

   If you’re setting up monitoring and want dashboards engineers actually use (not pretty charts that don’t help during incidents), this guide walks through Prometheus + Grafana fundamentals and focuses on building dashboards that are actionable for on-call, troubleshooting, and capacity planning: https://lnkd.in/eY9K4GFU The best dashboards follow a simple rule: start with questions engineers ask, then design panels that answer them fast. (Grafana’s own guidance and fundamentals align with this mindset.) ✅ What to include in engineer-grade dashboards Golden signals / RED: latency, traffic, errors, saturation Service health: availability, SLO burn, error-budget signals Infra & Kubernetes: CPU/memory, node pressure, pod restarts, throttling Dependencies: DB/cache/queue latency + error rates Alerts that matter: fewer, higher-signal alerts tied to impact ✅ Prometheus + Grafana done right Prometheus collects time-series metrics; Grafana visualizes them into dashboards and aler...

  If you’re struggling with slow incident recovery, noisy alerts, or unclear “who owns what” during outages, this step-by-step guide explains how to  reduce MTTR  using practical engineering habits:  playbooks, runbooks, alert tuning, and clear ownership —so on-call becomes predictable and incidents close faster. MTTR drops when response is  systematic , not heroic: ✅  Playbooks  for fast triage (what to check first, common failure patterns) ✅  Runbooks  for repeatable fixes (commands, rollback steps, known-good actions) ✅  Alert tuning  to kill noise (actionable alerts only, correct thresholds, dedup) ✅  Ownership  so issues don’t bounce between teams (service owners + escalation paths) ✅  Post-incident improvements  that prevent repeats (automation + guardrails) Read the full guide here: https://www.cloudopsnow.in/reduce-mttr-playbooks-runbooks-alert-tuning-and-ownership-the-engineers-step-by-step-guide/ #SRE #...

  If you’re running production systems,  incident response needs a playbook—not improvisation . This practical guide covers the end-to-end workflow:  on-call readiness, severity levels, clear stakeholder comms (with reusable templates), and blameless postmortems  so your team can reduce confusion, improve MTTR, and learn from every outage. ✅ What you’ll implement from this playbook: On-call structure:  roles, handoffs, escalation, and runbook habits Severity model:  SEV/P0 definitions tied to customer impact + response expectations Comms templates:  consistent updates for “Investigating → Identified → Monitoring → Resolved” Postmortems that improve reliability:  timeline, root cause, impact, and actionable follow-ups Read here: https://www.cloudopsnow.in/incident-management-on-call-severity-comms-templates-and-postmortems-the-practical-playbook/ #IncidentManagement #OnCall #SRE #DevOps #ReliabilityEngineering #Postmortem #RCA #Observability #Produ...