DevOps Training

  If you’re choosing an Infrastructure-as-Code tool and tired of marketing comparisons, this guide breaks it down in an engineer-first way—showing when  Terraform vs CloudFormation vs Pulumi  fits best, based on team skills, scale, governance needs, and day-to-day workflows (with practical decision criteria, not theory). Most teams don’t fail at IaC because the tool is “bad.” They fail because the tool doesn’t match how the team builds, reviews, secures, and operates infrastructure. ✅  Terraform  → best for multi-cloud + strong ecosystem + reusable modules ✅  CloudFormation  → best for AWS-native teams that want tight AWS integration + guardrails ✅  Pulumi  → best for dev-heavy teams that want IaC in real programming languages + shared app/platform patterns Read here: https://www.cloudopsnow.in/terraform-vs-cloudformation-vs-pulumi-which-fits-which-team-the-practical-engineer-first-guide/ #Terraform #CloudFormation #Pulumi #IaC #Infrastructur...

  If you’re an engineer using Terraform in a team (or CI/CD) and you’ve ever worried about  state corruption, drift, locking issues, or “who changed what” , this guide is built as a practical survival manual. It covers  remote state, state locking, drift detection, safe recovery, and real-world workflows  so you can operate Terraform confidently in production. Terraform becomes safe and scalable when you treat  state  like a first-class system: ✅  Remote State  → store state centrally (not on laptops) so teams and pipelines stay consistent ✅  Locking  → prevent concurrent applies that can corrupt infrastructure ✅  Drift  → detect when real infra diverges from code (and fix it safely) ✅  Recovery  → handle lost/invalid state, rollbacks, imports, and “bad apply” scenarios Read here: https://www.cloudopsnow.in/terraform-state-management-remote-state-locking-drift-recovery-the-engineers-survival-guide/ #Terraform #IaC #De...

  If you’re starting with Terraform (or you’ve used it but still feel shaky on “modules vs state vs workspaces”), this guide is a clean, engineer-friendly walkthrough that explains the fundamentals  with real examples —and shows how to build Terraform in a maintainable, production-ready way. Terraform becomes easy when you follow a simple path: ✅  Core concepts  → providers, resources, variables, outputs (and how plans really work) ✅  Modules  → reuse infrastructure like “packages” (structure, inputs/outputs, versioning) ✅  State  → why remote state matters, locking, drift, and safe workflows ✅  Workspaces  → when to use them (and when not to) for env separation ✅  Best practices  → naming, folder layout, secrets handling, CI/CD, linting/testing, and guardrails Read here: https://www.cloudopsnow.in/terraform-for-beginners-modules-state-workspaces-best-practices-with-real-examples/ #Terraform #IaC #DevOps #Cloud #AWS #Azure #GCP...

  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