LearnDevOps

  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 #ErrorBudgets #ReliabilityEngineering #DevOps #PlatformEngineering...

 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...

 If you’re managing multiple AWS accounts / Azure subscriptions / GCP projects , governance can quickly turn into chaos—different standards, inconsistent security, surprise bills, and “who changed what?” confusion. This guide shares a practical, step-by-step way to build scalable guardrails so teams can move fast without breaking compliance, security, or cost controls. ✅ What you’ll implement (real, scalable guardrails): A clean org structure (accounts/projects grouped by env, team, workload) Standard baselines for IAM, networking, logging, and monitoring Policy-as-code guardrails (prevent risky configs before they land) Cost guardrails (budgets, quotas, tagging rules, anomaly checks) Automated onboarding (new account/project setup in minutes, not days) Day-2 operations : drift detection, exception handling, and audit readiness Read the full step-by-step guide here: https://www.cloudopsnow.in/multi-account-multi-project-governance-guardrails-that-scale-practical-step-by-step...

 If you’re setting up cloud audit logging (AWS/Azure/GCP) and feel overwhelmed by what to log , how long to retain it , and when to alert , this engineer-friendly guide breaks it down step-by-step with practical use cases—so you can improve security and troubleshooting without drowning in noisy logs. Cloud Audit Logging — what actually matters: ✅ What to log (must-have) IAM/auth changes, privileged actions, policy edits Network/security changes (SG/NACL/firewall, public exposure) Data access events (storage reads, DB admin actions) Kubernetes + workload changes (deployments, secrets, config) ✅ Retention (simple rule of thumb) Short-term “hot” logs for investigations + debugging Longer retention for compliance + incident timelines Archive strategy so costs don’t explode ✅ Alerting that’s useful (not noise) Root/admin activity, unusual geo/logins Permission escalations, key creation, MFA disabled Sudden spike in denied actions or data downloads Changes to logging itself (tampering / ...

  If you’re setting up Kubernetes access for teams and want it to be  secure, least-privilege, and easy to maintain , this RBAC cookbook walks through  ready-to-use role patterns  for  Dev ,  SRE , and  Read-only  users—plus the common mistakes that accidentally grant too much power. Kubernetes RBAC gets messy fast unless you standardize it: ✅  Dev role  → limited to a namespace (deploy, view logs, exec only if needed) ✅  SRE role  → broader operational access (debug, scale, rollout, events) with guardrails ✅  Read-only role  → safe observability access (get/list/watch) without mutation rights ✅ Best practices →  avoid ClusterAdmin , prefer  Role + RoleBinding , review permissions, and validate with  kubectl auth can-i Read the full cookbook here: https://www.cloudopsnow.in/kubernetes-rbac-cookbook-common-roles-dev-sre-read-only-safely/ #Kubernetes #RBAC #DevOps #SRE #CloudNative #Security #PlatformEngi...