AWS EKS Auto Mode – a game changer for startups or organizations that prefer to focus on building products rather than managing Kubernetes clusters. This feature simplifies cluster operations, reduces management overhead, and lets teams scale efficiently without deep Kubernetes expertise. Well explained by Nana Janashia 👏 #AWS #EKS #Kubernetes #CloudComputing #DevOps

You can’t fix what you can’t see — and most teams don’t see drift until it breaks production. Every DevOps engineer has lived this story: The code works perfectly in QA. Fails in staging. Breaks in production. Someone says, “Nothing changed.” But something always did. That’s the silent chaos of configuration drift — subtle misalignments between environments that cause outages, compliance failures, or security gaps. Traditional monitoring tools catch it after it causes damage. By then, logs are messy, dashboards contradict each other, and everyone’s guessing. Cloudshot’s Real-Time Drift Map changes that. It visualizes every infrastructure change across AWS, Azure, and GCP — as it happens. You see what changed, when, and why — before it breaks anything. ✅ Detect configuration drift instantly ✅ Trace the impact to dependent systems ✅ Auto-highlight compliance deviations across accounts When visibility becomes live, control stops being reactive. That’s what Cloudshot makes possible — clarity before chaos. 👉 Explore the Real-Time Drift Map - https://lnkd.in/diXn4Qgi #CloudManagement #DevOps #InfrastructureAsCode #CloudSecurity #Cloudshot #MultiCloud

You define a PersistentVolumeClaim (PVC) and assume your data is safe. But then your pod crashes or worse, gets rescheduled to another node. Suddenly, your app won’t start. Logs are gone. The PVC is stuck in “Pending.” We think PVC = persistence. But PVCs are just "claims", not guarantees. The real behavior depends on the storage backend. - Node-attached volumes (like AWS EBS or Azure Disk) can’t follow pods across nodes easily. - PVCs can get stuck if no matching PV exists. - Wrong access modes or missing StorageClasses? Your PVC won’t bind. - And without proper QoS, your pods might get evicted under memory pressure. It’s not just frustrating, it’s risky. Especially in autoscaling environments where pods move frequently. Understand your storage backend: Use network-attached volumes (like NFS, EFS, CephFS) for flexible, multi-node access. - Define proper access modes: Use `ReadWriteMany` if your pods need shared access. - Use StorageClasses: Automate provisioning and match the right backend. - Combine with StatefulSets: For stable pod identities and persistent storage per replica. PVCs don’t make your data persistent, your architecture does. Design wisely, or the kernel will decide for you. I’m still exploring this, would love to hear how others approach PVCs and storage classes in real-world setups. 📌PS: If your team is building in Cloud and DevOps and needs Hands-on support or is hiring for DevOps engineer, feel free to reach out. I'd love to bring value to your team #Kubernetes #DevOps #DevOpsEngineer

🔍 New: Amazon CloudWatch Enhances Distributed Application Monitoring! 🎯 Key Features: • Automatic service discovery & grouping • Cross-account & cross-region visibility • Dynamic dependency mapping • Zero manual configuration needed 💡 Benefits: • Faster issue remediation • Clear visibility of service dependencies • Automatic blast radius assessment • Flexible organization by team/business unit • Enhanced APM capabilities Good for SREs and DevOps teams managing complex distributed systems! Makes troubleshooting easier with intelligent service organization and dependency visualization. 🚀 https://lnkd.in/dijaC9Gf

Every SRE or DevOps engineer knows this moment — dashboards flash red, latency spikes, alerts pour in. This time, it wasn’t our code. It was AWS US-EAST-1, the heartbeat of countless production workloads. It started with failed API calls, long response times, and sudden drops in throughput. Within minutes, we knew this wasn’t a local issue — a regional disruption was unfolding. Here’s how the SRE team managed the chaos 👇 🔹 Detection & Communication The alerts were our first clue — CloudWatch, Datadog, and Grafana lit up simultaneously. We declared an incident, spun up a bridge, and began triaging. The AWS status dashboard soon confirmed elevated error rates across key services. 🔹 Isolation & Failover To minimize customer impact, we rerouted traffic to secondary regions via Route 53, temporarily froze deployments, and prioritized critical user flows. Automated failover scripts kicked in, and we validated service health in US-WEST-2 before shifting load. 🔹 Collaboration & Visibility While AWS engineers worked on root cause mitigation, we focused on observability — refining dashboards, tracing dependencies, and identifying which microservices were most impacted. Real-time updates were shared across Slack, keeping product and leadership informed. 🔹 Recovery & Validation As AWS restored functionality, we carefully rolled back traffic to US-EAST-1, testing every API endpoint and pipeline job. Once stability was confirmed, we cleared the ingestion backlog and monitored the system for post-recovery anomalies. 🔹 Lessons & Reinforcement Post-incident, we reviewed every alert, automation trigger, and failover timeline. We strengthened multi-region architecture, improved alert correlation, and updated our incident playbook with new learnings. 🔹 Key takeaway: Outages will happen. What defines reliability isn’t the absence of incidents, it’s how quickly and calmly you recover. SREs aren’t firefighters; we’re engineers building systems resilient enough to survive the fire. 😎 #AWS #SRE #SiteReliabilityEngineering #DevOps #CloudComputing #IncidentResponse #Observability #ReliabilityEngineering #MultiRegion #Failover #Automation #CloudOps #OpsLife #C2C #C2H #USITReecruiters #TalentAcquisition #OpenToWork #USAJobs #USA

𝐂𝐥𝐨𝐮𝐝 𝐛𝐢𝐥𝐥𝐬 𝐚𝐫𝐞𝐧’𝐭 𝐡𝐢𝐠𝐡 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐀𝐖𝐒 — 𝐭𝐡𝐞𝐲’𝐫𝐞 𝐡𝐢𝐠𝐡 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐨𝐟 𝐮𝐬. 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 𝐭𝐚𝐥𝐤𝐬 𝐚𝐛𝐨𝐮𝐭 “𝐫𝐢𝐠𝐡𝐭-𝐬𝐢𝐳𝐢𝐧𝐠 𝐢𝐧𝐬𝐭𝐚𝐧𝐜𝐞𝐬” 𝐨𝐫 “𝐮𝐬𝐢𝐧𝐠 𝐬𝐩𝐨𝐭 𝐦𝐚𝐜𝐡𝐢𝐧𝐞𝐬.” 𝐓𝐡𝐚𝐭’𝐬 𝐬𝐮𝐫𝐟𝐚𝐜𝐞-𝐥𝐞𝐯𝐞𝐥. Real cost optimization starts in engineering. As DevOps engineers, the way we build and run workloads directly impacts spend:  • Bloated Docker images → Bigger images increase storage and slow CI/CD, driving higher compute and network costs  • Unoptimized pipelines → Rebuilding unchanged layers or running redundant tests wastes paid runner time  • Excess artifacts & logs → Storing gigabytes forever in S3 or EFS adds unnecessary storage charges  • Idle clusters & over-provisioned nodes → Low pod density means paying for unused compute 𝘊𝘭𝘰𝘶𝘥 𝘤𝘰𝘴𝘵 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘢𝘵𝘪𝘰𝘯 𝘪𝘴𝘯’𝘵 𝘫𝘶𝘴𝘵 𝘧𝘪𝘯𝘢𝘯𝘤𝘦. 𝘐𝘵’𝘴 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘥𝘪𝘴𝘤𝘪𝘱𝘭𝘪𝘯𝘦. 𝘌𝘷𝘦𝘳𝘺 𝘔𝘉, 𝘣𝘶𝘪𝘭𝘥, 𝘢𝘯𝘥 𝘱𝘰𝘥 𝘤𝘰𝘶𝘯𝘵𝘴. #DevOps #Cloud #FinOps #AWS #CostOptimization #Kubernetes #EngineeringExcellence

🉑 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 — Explained Simply ✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ Scaling in Kubernetes isn’t one-size-fits-all. Depending on your workloads and metrics, you can scale pods, nodes, or entire clusters — automatically or manually. Here are the 6 main strategies every DevOps engineer should know 👇 1️⃣ Horizontal Pod Autoscaling (HPA) → Adds more Pods when CPU/memory usage increases. 2️⃣ Vertical Pod Autoscaling (VPA) → Adjusts Pod resources up or down. 3️⃣ Cluster Autoscaling → Adds or removes Nodes automatically. 4️⃣ Manual Scaling → Using kubectl scale when full control is needed. 5️⃣ Predictive Scaling → Uses ML (like KEDA) to anticipate future demand. 6️⃣ Custom Metrics Scaling → Scales Pods based on business or app-specific metrics. 💡 Smart scaling = performance + cost efficiency + stability. 💚 I ❤️ | 💙 I 💚 ☸️ #Kubernetes #DevOps #Autoscaling #CloudNative #CKA

🉑 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬 — Explained Simply ✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ Scaling in Kubernetes isn’t one-size-fits-all. Depending on your workloads and metrics, you can scale pods, nodes, or entire clusters — automatically or manually. Here are the 6 main strategies every DevOps engineer should know 👇 1️⃣ Horizontal Pod Autoscaling (HPA) → Adds more Pods when CPU/memory usage increases. 2️⃣ Vertical Pod Autoscaling (VPA) → Adjusts Pod resources up or down. 3️⃣ Cluster Autoscaling → Adds or removes Nodes automatically. 4️⃣ Manual Scaling → Using kubectl scale when full control is needed. 5️⃣ Predictive Scaling → Uses ML (like KEDA) to anticipate future demand. 6️⃣ Custom Metrics Scaling → Scales Pods based on business or app-specific metrics. 💡 Smart scaling = performance + cost efficiency + stability. 👉 Follow me on LinkedIn : https://lnkd.in/d7BGZPw6 ✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧✦ ✧ ☸️ #Kubernetes #DevOps #Autoscaling #CloudNative #CKA #KubeAstronaut

What hiring managers want in Cloud & DevOps From my own research and conversations with senior engineers who’re helping me, one pattern is clear: teams don’t hire tool collectors, they hire T-shaped engineers. T-shaped = Broad across the delivery pipeline (Linux, Git, networking, CI/CD, IaC, cloud). Deep in 1-2 areas that solve real problems for the team. Great combos (now & next) and why DevOps + SRE/Observability - uptime, SLOs, incident response. Show it: Prometheus/Grafana dashboards, alerts, 5-line postmortem, error budgets. DevOps + Cloud Security (DevSecOps) — ship fast and safe. Show it: OIDC in CI, image/IaC scans (Trivy), least-privilege IAM, secret management, signed images. DevOps + Platform Engineering (IDP) - paved paths for developers. Show it: small developer portal, golden templates, Helm charts, “day-1/day-2” docs. DevOps + FinOps - cost is a feature. Show it: tagging strategy, auto-sleep non-prod, rightsizing before/after, budget alerts. DevOps + Cloud Networking — most prod issues = DNS/route/egress. Show it: hub-spoke VPC/VNet, private endpoints, SG/NSG, a simple network runbook. DevOps + Data/AI (LLMOps/MLOps) - AI needs reliable infra & evaluation. Show it: containerized LLM/RAG service with CI/CD, eval tests, safe rollout. Signals hiring managers love (portfolio > promises) A Kubernetes homelab with Ingress, TLS, HPA, logs/traces. A CI/CD pipeline (build → test → scan → deploy) with rollback. Terraform modules + diagram + README (what/why/how). Runbooks and a short postmortem showing how you think. Clear notes on cost, security, and reliability trade-offs. Avoid: only certifications, switching tools weekly, “Kubernetes without fundamentals.” Do: pick one combo, ship a small project in 2-4 weeks, and document your decisions. Which combo fits you best right now? #Cloud #DevOps #SRE #PlatformEngineering #DevSecOps #FinOps #Networking #LLMOps #MLOps #LearningInPublic

🚀 Reliability isn’t built — it’s earned. Every alert, every outage, every post-mortem teaches us one truth: Uptime isn’t just about servers; it’s about culture. In modern SRE, we don’t chase “zero incidents.” We chase resilience — systems that heal, teams that learn, and pipelines that adapt. ✅ Define SLIs/SLOs that reflect business impact ✅ Automate recovery through IaC & self-healing workflows ✅ Observe everything — logs, metrics, traces, user journeys ✅ Run chaos experiments before real chaos hits True reliability is when your systems stay calm under stress — and your engineers do too. That’s the art of blending DevOps + SRE + Automation. #SRE #DevOps #ReliabilityEngineering #CloudOps #DevSecOps #AWS #Azure #GCP #Terraform #Kubernetes #Observability #Automation #Resilience #Leadership