Navjot Singh

🚫 Stop confusing between Terraform and Ansible 🚫 Imagine you’re setting up a new house: 🏗️ Terraform is the architect and builder. • It lays the foundation, builds the walls, and sets up the wiring. • You just tell it what the house should look like (declarative), and it ensures it’s built that way. • It works across multiple clouds – AWS, Azure, GCP – like a universal contractor. • It also keeps track of what’s been built (state management), so changes are consistent. 🛠️ Ansible is the interior designer and caretaker. • Once the house exists, Ansible decorates it, installs appliances, and keeps things running. • It works step by step (procedural), following playbooks like a to-do list. • No agents needed – just simple SSH/WinRM. • Perfect for software installs, updates, patching, and everyday automation. 👉 Together: Terraform builds the house, Ansible makes it livable and well-maintained. ⸻ #DevOps #Terraform #Ansible #CloudComputing #Automation #InfrastructureAsCode #ConfigurationManagement #TechSimplified

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Before You Ship to Production… Read This. Too many teams treat Kubernetes like it’s still running in staging. Then comes the late-night pager. Downtime. Blame games. After mentoring dozens of engineers and seeing real outages firsthand, I have documented the 10 Non-Negotiables for Running Kubernetes in Production, no fluff, just hard-earned lessons. From probes to autoscaling, from secrets to security, this is the checklist you never skip. Follow Bavithran M for more DevOps, Kubernetes, and cloud-native insights. 🔁Don't keep this to yourself, share it with your network and help someone improve their Kubernetes resilience! #kubernetes #devops #cloudnative #productionready #platformengineering #careerbytecode CareerByteCode #bavicnative #DevSecOps #Azure #AWS #GCP #Job #Interview #Career #Linkedin #Troubleshooting

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💼 Just found this goldmine for anyone preparing for AWS interviews — and I had to share it. From IAM, EC2, and S3 to VPC, Route 53, and CloudFormation… this resource covers real interview questions with smart, structured answers you can actually use to stand out. Whether you’re a beginner or brushing up for your next role, this is one of those GameChangers that saves you hours of searching. 👉 Follow me for more DevOps, AWS, and tech career resources that actually make a difference. #AWS #CloudCareers #DevOps #AWSInterview #CloudComputing #AWSJobs #TechTips #GameChanger #AWSCommunity #CloudEngineer

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🚀 New in Kubernetes v1.33: trafficDistribution: PreferClose Now Generally Available (GA) Kubernetes v1.33 introduces a valuable update to Service networking: ✅ trafficDistribution: PreferClose is now stable and ready for production use. 🔍 What it does: Optimizes traffic routing by preferring endpoints closer to the client (same node or zone) Helps reduce latency and cross-zone network traffic Ensures availability by falling back to other endpoints if needed 📌 Key Highlights: 🟢 Available from Kubernetes v1.33+ 🔴 Not available in v1.32 and earlier 💼 Supports: ClusterIP, NodePort, and LoadBalancer services ⚙️ No feature gate or annotations required 📦 Works seamlessly with EndpointSlice topology data 💡 A great addition for: Multi-zone clusters Latency-sensitive applications Teams focused on cost and performance optimization Kubernetes continues to evolve with smarter traffic control — helping teams scale with better efficiency. #Kubernetes #K8s #Kubernetes133 #CloudNative #DevOps #Networking #PlatformEngineering #TrafficOptimization #PreferClose #SRE #KubernetesUpdate #TechNews

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You’d be surprised → 95% of DevOps and SRE engineers use Kubernetes daily but still can’t clearly explain how node affinity works in a deployment.yaml during real-world debugging or interviews. That’s why I broke down how Node Affinity helps control which nodes your pods get scheduled on → using simple matchExpressions, requiredDuringSchedulingIgnoredDuringExecution, and label selectors inside affinity blocks. Understand how to ensure your workloads run on the right nodes, whether for cost optimization, high availability, or hardware constraints. A must-read if you're working with Kubernetes in production or prepping for DevOps interviews. ♻️ If you found this helpful, consider reposting or sharing it with your network. Follow for more → Amay Jaiswal #Kubernetes · #DevOps · #SRE · #K8sDeployment · #NodeAffinity · #YAMLFiles · #CloudNative · #DevOpsInterview · #LinuxForDevOps · #InfrastructureAsCode · #KubernetesForBeginners · #DevOpsCommunity · #Pod

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Why Linux SHOULD Be Your First Skill Before Kubernetes. Before Docker. Before AWS. Start with Linux. Seriously. Here’s why it actually matters: 1. Everything runs on Linux Cloud servers, containers, orchestration tools - most of it is Linux underneath. If you don’t know it, you’re just guessing. 2. You’ll debug faster Logs, permissions, processes, they all live in the shell. Knowing Linux means you can fix things yourself instead of waiting on someone else. 3. It builds real confidence Once you get what’s happening under the hood, learning Docker or Kubernetes feels way less scary. 4. It’s the DevOps foundation CI/CD pipelines, automation scripts, monitoring agents, they all touch Linux at some point. Start small: • File system basics • Permissions • Networking • Shell scripting Then build up from there. Trust me. Everything else will make more sense. #devops #linux #cloudcomputing #interviewtips

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If you’re aiming to grow in Cloud or DevOps, Terraform is one skill that continues to stay in-demand. And across different roles, it’s used in different ways 👇 → Automating infrastructure provisioning with Terraform scripts (spin up servers and networks fast) → Building reusable IaC templates for consistent environments across dev, stage, prod (avoid manual setup errors) → Managing state files and remote backends for drift detection (track infra changes over time) → Optimizing Terraform workflows with CI/CD integrations ( enable automated transitions across different stages) Each use case adds depth — and helps you understand why it’s used in the first place. Currently, Terraform remains one of the strongest IaC skills to have in your toolkit. Found a solid list of 200+ Terraform Interview Questions — real-world use cases, CI/CD, state management, and updated for the latest version. Check it out.

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𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐎𝐛𝐣𝐞𝐜𝐭𝐬 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐈𝐧 𝟏𝟓 𝐒𝐢𝐦𝐩𝐥𝐞 𝐁𝐥𝐨𝐜𝐤𝐬 🧱 Kubernetes is built from small parts that work together. Each object has a job. Learn what they do — and why they matter. 1. Pod – The smallest unit. Runs your app containers. 2. Deployment – Keeps your pods running and updates them safely. 3. ReplicaSet – Makes sure the right number of pods are always running. 4. StatefulSet – Like Deployment, but for apps that need stable storage or names. 5. Job – Runs a task once, then stops. Great for batch work. 6. DaemonSet – Runs one pod on every node. Good for logging or monitoring. 7. Service – Gives your pods a stable way to talk to each other. 8. Ingress – Lets traffic from outside reach your app. 9. Network Policies – Control who can talk to whom inside the cluster. 10. Volume – Temporary storage for pods. 11. Persistent Volume – Long-term storage that survives pod restarts. 12. Secret – Stores sensitive data like passwords. 13. Namespace – Splits the cluster into groups. Helps with isolation. 14. Service Account – Gives pods an identity to access the API. 15. Roles – Controls what users or pods are allowed to do. Learn these, and you’ll understand how Kubernetes really works. #devops #interviewtips #kubernetes #cloudcomputing

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𝟯 𝗦𝘁𝗲𝗽𝘀 𝗧𝗼 𝗦𝗲𝘁𝘁𝗶𝗻𝗴 𝗨𝗽 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝘂𝗱 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺: Most Terraform repos 𝘤𝘰𝘭𝘭𝘢𝘱𝘴𝘦 the second you add more than one Cloud. Here’s the 3-part structure that keeps 𝐦𝐮𝐥𝐭𝐢-𝐜𝐥𝐨𝐮𝐝 Terraform clean and scalable 👇🏽 1️⃣ 𝐂𝐥𝐨𝐮𝐝-𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐜𝐨𝐝𝐞 ▶ multi-cloud-terraform/ holds separate folders for 𝐀𝐖𝐒 and 𝐀𝐳𝐮𝐫𝐞. ▶ Each has its own 𝐦𝐚𝐢𝐧.𝐭𝐟, 𝐯𝐚𝐫𝐢𝐚𝐛𝐥𝐞𝐬.𝐭𝐟, and 𝐨𝐮𝐭𝐩𝐮𝐭𝐬.𝐭𝐟. ▶ This keeps the logic for each provider clear and isolated. 2️⃣ 𝐑𝐞𝐮𝐬𝐚𝐛𝐥𝐞 𝐦𝐨𝐝𝐮𝐥𝐞𝐬 ▶ Inside modules/, you define building blocks like: AWS storage Azure compute Shared networking ▶ Write once, use many times. No duplication. 3️⃣ 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭 𝐜𝐨𝐧𝐟𝐢𝐠𝐬 ▶ envs/ contains dev, test, and prod. ▶ Each environment has its own backend, variables, and provider files. ▶ Same codebase, different settings, isolated deployments. 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬: ✅ No duplication across clouds ✅ Teams scale without stepping on each other ✅ Environments stay clean and consistent 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 𝗶𝘀 𝗽𝗼𝘄𝗲𝗿𝗳𝘂𝗹, 𝗯𝘂𝘁 𝗼𝗻𝗹𝘆 𝗶𝗳 𝘆𝗼𝘂 𝗱𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝗴𝗿𝗼𝘄𝘁𝗵. 𝗛𝗼𝘄 𝗵𝗮𝘃𝗲 𝘆𝗼𝘂 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀?

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Kubernetes Made Simple: What Happens Under the Hood ⚙️ Exploring the orchestration journey of a "Create Pod" operation in Kubernetes sheds light on the system's inner workings. Here's a breakdown of the process: 1️⃣ User triggers a "Create Pod" request to the API Server. 2️⃣ The API Server delegates the request to etcd for state storage. 3️⃣ The Scheduler oversees and plans: - 3.1 Identifies and picks Node 2 ✅ - 3.2 Updates the API Server with the assignment. 4️⃣ The API Server directs kubelet on Node 2 to execute the Pod. 5️⃣ The Container runtime on Node 2 sets up the Pod, configuring images, network, and volumes. 6️⃣ kubelet confirms "Pod created," updates the API Server, and notifies the user (Task completed! 🎉 ) Understanding the sequence of a "Create Pod" operation's orchestration in Kubernetes can boost team collaboration and deepen system understanding. #Kubernetes #Containerization #DevOps #CloudComputing #Containers #CloudNative #DevOpsLife #TechGuide #CloudEngineer #K8s #PlatformEngineering #SiteReliabilityEngineering #CloudInfrastructure #KubernetesLearning #KubernetesCommunity #CloudSolutions #OpsLife #KubernetesTips

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Why does every senior DevOps engineer tell you: Learn Linux first? Before Kubernetes, before AWS, before Terraform… learn Linux. Here’s why 👇 Because no matter how shiny the cloud console looks, every outage eventually drags you down to the command line. 1. Kubernetes Pod failing? → You’ll debug with kubectl exec and land in Linux. 2. Pipeline broken? → File permissions, symlinks, or shell quirks. 3. High CPU alert? → top, iostat, vmstat — Linux tools, not AWS dashboards. 4. Disk full at 2 AM? → du -sh, df -h, lsof | grep deleted. The reality: AWS UI can help you start. Python scripts can make you look smart. But only Linux will save you when prod is burning. If you’re starting DevOps, learn these first: 1. grep, awk, sed → text wrangling 2. ps, lsof, strace → process forensics 3. netstat, ss, tcpdump → network truth serum 4. systemctl, journalctl → service lifelines Everything else in DevOps - Kubernetes, Terraform, CI/CD - is built on top of Linux. Skip this step, and you’ll always feel like an imposter. If you want to be taken seriously as a DevOps engineer, Learn Linux first. Everything else comes later. ⭕️ If this helps, consider a repost #LearnLinux #DevOps #ZeroToDevOps #SRE #InfraThrone #DevOps

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if a Kubernetes cluster went down. 200+ pods across 20 services crashed simultaneously. Frantically tailing random logs from different services Checking CPU, memory, disk space Restarting pods one by one hoping something would magically work Opening 5 different monitoring dashboards at once But the noise was overwhelming. Nothing obvious stood out. Then came a pause. try checking   What does the event log show for the failing pods? kubectl get events --sort-by='.lastTimestamp' | head -20 This shows the 20 most recent events in your cluster, sorted by timestamp. Discovers a new ConfigMap deployment removed a critical environment variable that one services depend on. And there it was: All the failures were BackOff issues. Not infra, not resource limits, not a node problem. A new ConfigMap deployment had removed a critical environment variable. One service depended on it, broke internally, and triggered a chain reaction across dependent pods. you will see this one Warning BackOff pod/service-7fdf4d9df6-xyz12 Back-off restarting failed container Thanks Akhilesh Mishra for sharing this real-world scenario — a great reminder of how small changes can help resolve chaos.

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