DevOps Training

   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...

  If you’re struggling to turn “99.9% uptime” into something engineers can actually  run , this guide breaks down  SLI → SLO → Error Budgets  in a practical, step-by-step way—so you can choose the right user-focused metrics, set realistic targets, and use error budgets to balance reliability with feature velocity (the core approach promoted in Google’s SRE guidance). CloudOpsNow article: https://www.cloudopsnow.in/sli-slo-error-budgets-create-slos-that-actually-work-step-by-step-with-real-examples/ Quick takeaway (engineer-friendly): ✅ Pick  critical user journeys  → define SLIs that reflect user experience (latency, availability, correctness) ✅ Set  SLO targets + window  (e.g., 30 days) and compute the  error budget  (for 99.9%, that’s ~43 minutes in 30 days) ✅ Track  error budget burn  and use it to drive decisions: ship faster when you’re healthy, slow down and fix reliability when you’re burning too fast #SRE #SLO #SLI #Err...

 If you’re planning to adopt  OpenTelemetry  and don’t want it to turn into a messy, “instrument-everything-and-pray” rollout, this practical guide breaks down a calm, step-by-step way to introduce OTel with the right standards, rollout strategy, and guardrails—so you get reliable traces/metrics/logs without chaos. OpenTelemetry adoption works best when you treat it like an engineering migration: ✅ Start with 1–2 critical services (not the whole platform) ✅ Standardize naming + attributes early (service.name, env, version, tenant) ✅ Use  OTel Collector  as the control plane (routing, sampling, processors, exporters) ✅ Decide what matters: golden signals, key spans, and cost-safe sampling ✅ Roll out in phases: baseline → dashboards → alerts → SLOs → continuous improvements ✅ Measure overhead + data volume so observability doesn’t become the new bill shock Read the full guide here: https://www.cloudopsnow.in/opentelemetry-practical-guide-how-to-adopt-without-chaos...