DevOps Training

  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...

  If you’re building or running systems in production and wondering why incidents still feel “invisible,” this article is a clean, beginner-friendly  Observability 101  guide that explains  Logs vs Metrics vs Traces  in plain English—and, more importantly, tells you  what to instrument first  so you get the fastest debugging wins without boiling the ocean. Observability isn’t “add more dashboards.” It’s having the right signals when things break: ✅  Metrics  →  What’s wrong?  (latency, errors, saturation, throughput) ✅  Logs  →  What happened?  (events + context, structured logging) ✅  Traces  →  Where is it slow/broken?  (end-to-end request path across services) A solid order to start: Golden Signals / RED  metrics first Add  structured logs  with correlation IDs Instrument  distributed tracing  for critical flows Read the full guide here: https://www.cloudopsnow.in/o...

 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 Ch...

  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...