Kyryl Skobylko

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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“How do you recover from a deleted Terraform state file?” is another common DevOps interview question. Here’s how to answer it correctly. What happens when state file is delete? Terraform loses track of all infrastructure it created. Running terraform plan will show it wants to create everything again, even though resources already exist. Running terraform apply will fail because resources already exist in the cloud. You’re basically stuck. Terraform can’t manage what it can’t track. How do you recover ? Option 1: Restore from backup( If at all possible) —> If you’re using remote state (S3, Terraform Cloud), check for versioning. S3 with versioning enabled keeps old versions of your state file. —> Go to S3, find the deleted state file, and restore the previous version. Option 2: Recreate state using terraform import —> If you have no backup, you need to manually import every resource back into Terraform. —> For each resource in your Terraform code, run: terraform import aws_instance.my_server i-1234567890abcdef0 —> This tells Terraform “hey, this EC2 instance already exists, track it.” —> You’ll need to import every single resource. It’s time-consuming and error-prone. Option 3: Start fresh (last resort) —> Destroy everything manually in the cloud console. —> Run terraform apply to recreate from scratch. —> Only do this in non-production or if downtime is acceptable. Best practices to avoid this disaster: 👉Use remote state with versioning - Store state in S3 with versioning enabled, or use Terraform Cloud. - Never store state files locally for production infrastructure. 👉Enable state file backups - S3 versioning keeps multiple versions automatically. - Set up lifecycle policies to retain versions for 30+ days. 👉 Use state locking - Prevents multiple people from modifying state simultaneously. - DynamoDB for S3 backend, or built-in for Terraform Cloud. 👉Regular state backups - Even with remote state, periodically backup your state file separately. - Store encrypted backups in a different location. ✅Best answer in an interview: If the state file is deleted, I’d first check if we have versioning enabled on our remote state backend like S3. I’d restore the previous version immediately. If there’s no backup, I’d need to manually import each resource back into Terraform using `terraform import`, which is time-consuming. To prevent this, I always use remote state with versioning enabled, enable state locking with DynamoDB, and never store state files locally or in Git. The state file is critical infrastructure, so treating it like production data is essential.” That’s it. It shows you understand the problem, know multiple recovery options, and follow best practices to prevent it. . . . If you found it useful then you will love my newsletter where I share real world Devops related content. Subscribe here: https://lnkd.in/gaca-kQS

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😩 Ever battled with YAML indentation errors, unexpected type coercions, or Git diffs that look like hieroglyphics? Kubernetes is finally giving us a solution: KYAML 🎉 A safer, stricter, and more predictable way to write manifests. ✨ Key highlights: Explicit strings → "v1", "web" (no more hidden coercions) JSON-like braces {} and [] → more structure, less whitespace magic Trailing commas allowed → cleaner diffs, easier editing Consistent parsing → fewer “works on my machine” surprises 👉 Starting with Kubernetes v1.34, you can try it today: kubectl get svc -o kyaml This won’t replace YAML overnight, but it’s a huge step toward reliability, automation safety, and less time wasted fixing silly whitespace issues. Less ambiguity. More safety. Cleaner GitOps. 🚀 #Kubernetes #DevOps #GitOps #PlatformEngineering #K8s #KYAML #Automation #InfrastructureAsCode #CloudNative #YAML #OpenSource #SRE #PlatformEngineer #KubernetesUpdate #TechNews #IaC

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Understanding Kubernetes Architecture Kubernetes is a game-changer for managing containerized applications. Its architecture is divided into two core components: 1. Control Plane: 👉 API Server: The management hub of the cluster. 👉 etcd: A key-value store holding cluster state. 👉 Scheduler: Decides where Pods run. 👉 Controller Manager: Ensures desired states match actual states. 2. Worker Nodes: These are the machines (virtual or physical) where your applications (in the form of Pods) actually run. Kubelet: The node agent that ensures containers are running in a Pod as expected. It’s the worker bee that takes orders from the API Server and gets the job done. Kube Proxy: Manages networking for the Pods, ensuring they can communicate with each other and the outside world. It’s the traffic cop of Kubernetes, directing network traffic efficiently. Container Runtime Interface (CRI): The underlying software that actually runs your containers. This could be Docker, containerd, or another container runtime. It’s the engine that runs the applications within Pods. 3. Communication Tools: Kubectl: The command-line tool that you use to interact with your Kubernetes cluster. Whether you’re deploying applications or checking the health of your cluster, Kubectl is your go-to tool. Cloud Provider API: Allows Kubernetes to interact with cloud resources like load balancers, storage, and more. It’s how Kubernetes speaks to the cloud. 𝐃𝐌 me "roadmap" if you're serious about your cloud career and ready to fast-track your results. 𝐅𝐫𝐞𝐞 𝐀𝐖𝐒 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: Check comments Follow Riyaz Sayyad for more AWS Cloud content

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5 Levels of Kubernetes. - level 1 Knows basic concepts. Limited troubleshooting skill. - level 2 Managing deployments, namespaces & scaling apps. Troubleshoots issues with pods and deployments. - level 3 Deep understanding of Kubernetes. Automate deployments. Debug advanced issues. - level 4 Optimizes cluster performance. Follow Kubernetes security best practices. Designs & implements secure and scalable clusters. - level 5 Ability to say - "We don't need Kubernetes for this problem." So, what do you think? Am I missing anything? 🔁 Do a repost if this is helpful.

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𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐬𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝: 𝐓𝐡𝐞 𝐛𝐫𝐚𝐢𝐧 𝐚𝐧𝐝 𝐦𝐮𝐬𝐜𝐥𝐞 𝐛𝐞𝐡𝐢𝐧𝐝 𝐦𝐨𝐝𝐞𝐫𝐧 𝐚𝐩𝐩𝐬 90% of companies running cloud-native applications today rely on Kubernetes. But very few actually understand how its internal architecture works. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐚 𝐡𝐢𝐠𝐡-𝐥𝐞𝐯𝐞𝐥 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐲𝐨𝐮 𝐜𝐚𝐧𝐧𝐨𝐭 𝐦𝐢𝐬𝐬: 𝟏. 𝐌𝐚𝐬𝐭𝐞𝐫 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 (𝐓𝐡𝐞 𝐁𝐫𝐚𝐢𝐧) - API Server (REST API): The entry point for all requests. Developers talk to Kubernetes here. - etcd (Key-Value DB): Stores the cluster state which pod runs where, configs, secrets. Think of it as Kubernetes’ memory. - Scheduler: Decides which node gets which pod based on resources and rules. - Controller Manager: Keeps everything running as expected by watching and reconciling desired vs actual state. 𝟐. 𝐍𝐨𝐝𝐞 𝐂𝐨𝐦𝐩𝐨𝐧𝐞𝐧𝐭𝐬 (𝐓𝐡𝐞 𝐌𝐮𝐬𝐜𝐥𝐞) 𝐄𝐚𝐜𝐡 𝐧𝐨𝐝𝐞 𝐢𝐬 𝐚 𝐰𝐨𝐫𝐤𝐞𝐫 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐫𝐮𝐧𝐧𝐢𝐧𝐠 𝐭𝐡𝐞 𝐚𝐜𝐭𝐮𝐚𝐥 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬. 𝐈𝐧𝐬𝐢𝐝𝐞 𝐞𝐯𝐞𝐫𝐲 𝐧𝐨𝐝𝐞: - Kubelet: Talks to the master and makes sure containers are healthy and running. - Container Runtime (Docker, containerd, etc): Runs the containers themselves. - Networking: Handles pod-to-pod and pod-to-service communication. - OS + Hardware: The foundation that provides CPU, memory, and storage. 𝟑. 𝐇𝐨𝐰 𝐈𝐭 𝐀𝐥𝐥 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐬 Users send commands → API Server validates → Scheduler places workloads → Nodes execute via Kubelet and Container Runtime → etcd keeps track of the truth. This master-worker model ensures apps scale automatically, recover from failures, and remain resilient. --- 𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 Kubernetes is not just a DevOps tool, it’s the operating system of the cloud. If you are in software, DevOps, or cloud engineering, mastering Kubernetes is no longer optional it’s foundational. Is your team running Kubernetes clusters in production today, or are you still experimenting in dev/test environments? ♻️ Repost this to help your network get started ➕ Follow Jaswindder Kummar for more #Kubernetes #DevOps #CloudComputing #Containers

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100 Linux Interview Questions that will land you in your next DevOps/Cloud EngineerRole 🐧 51. How do you set environment variables? 52. What is the `PATH` variable? 53. How do you make environment variables persistent? 54. What is the difference between `export` and `set`? 55. How do you view all environment variables? 56. What is cron in Linux? 57. How do you schedule a cron job? 58. What is the format of crontab? 59. How do you list current cron jobs? 60. What is `at` command used for? 61. Where are system logs stored? 62. What is `syslog`? 63. How do you monitor log files in real-time? 64. What is log rotation? 65. How do you search logs for specific patterns? 66. How do you install packages using `apt`? 67. What is `yum` used for? 68. How do you update all packages? 69. What's the difference between `apt` and `apt-get`? 70. How do you remove packages? 71. What is a shell script? 72. How do you make a script executable? 73. What is the shebang line? 74. How do you pass arguments to a script? 75. What are conditional statements in bash? 76. How do you mount and unmount filesystems? 77. What is `/etc/fstab`? 78. How do you check filesystem disk space? 79. What is `fsck` used for? 80. How do you create and manage users? 81. How do you troubleshoot high CPU usage? 82. What is `iostat` used for? 83. How do you monitor network traffic? 84. What is `vmstat`? 85. How do you analyze memory usage? 86. How do you secure SSH connections? 87. What is `sudo` and how does it work? 88. How do you configure firewall rules? 89. What is SELinux? 90. How do you check for rootkits? 91. How do you configure static IP addresses? 92. What is `iptables`? 93. How do you set up port forwarding? 94. What is network bonding? 95. How do you troubleshoot DNS issues? 96. What is the difference between containers and VMs? 97. How do you install Docker on Linux? 98. What are Linux namespaces? 99. How do you manage Linux containers? 100. What is systemd and how does it work? #Linux #DevOps #CloudComputing #TechInterviews #ITJobs #CloudCareers #TechCareers #InterviewPrep #LinuxInterview #DevOpsInterview #CloudJobs #TechSkills #SystemAdministration #CloudEngineer #ITCareers #TechLearning

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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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Terraform interviews don’t test your memory, they test how you’d survive production at 2 AM. When I was preparing for DevOps interviews, I realized something important: 𝐍𝐨 𝐨𝐧𝐞 𝐫𝐞𝐚𝐥𝐥𝐲 𝐚𝐬𝐤𝐬 𝐲𝐨𝐮 𝐭𝐨 “𝐝𝐞𝐟𝐢𝐧𝐞 𝐓𝐞𝐫𝐫𝐚𝐟𝐨𝐫𝐦.” Instead, they ask: “How would you set up Terraform with CI/CD? What happens if your state file is locked? How do you ensure security in infra-as-code?” And trust me, these aren’t trick questions. They’re real-world challenges. Recently, I worked on a project that reshaped how I think about Terraform in production: 🔹 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐂𝐈/𝐂𝐃 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞𝐬 Instead of manual terraform apply, I built pipelines using AWS CodePipeline + CodeBuild, triggered directly from GitHub commits. 🔹 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 Integrated the Terraform Test Framework for unit, integration, and end-to-end tests—because broken infra in prod is 100x costlier than a broken build. 🔹 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐅𝐢𝐫𝐬𝐭 Added Checkov scans and enforced TFLint, ensuring code was not only functional but also compliant and secure. 🔹 𝐑𝐞𝐦𝐨𝐭𝐞 𝐒𝐭𝐚𝐭𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 Configured S3 + DynamoDB for safe, collaborative state handling. No more overwriting each other’s infra by mistake. 🔹 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 From ARN mismatches to webhook failures, I learned that Terraform CI/CD is as much about debugging as it is about design. The biggest takeaway? Terraform isn’t just “infra as code.” It’s about building repeatable, secure, and scalable workflows that your team can trust in production. If you’re preparing for DevOps interviews, don’t just memorize commands. Think about real scenarios: testing, pipelines, state, and security—that’s what hiring managers truly look for.

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𝟱 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀 𝗘𝘃𝗲𝗿𝘆 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 🚀 When something goes wrong in Kubernetes, these are usually my first stops. They save time, reduce guesswork, and often point straight to the root cause. 🔹𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐠𝐞𝐭 𝐩𝐨𝐝𝐬 Quick health check - see if your pods are running, pending, or crashing. 🔹 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞 𝐩𝐨𝐝 <𝐩𝐨𝐝> Detailed info: events, reasons for failures, resource issues - basically your pod’s “autopsy report.” 🔹 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐥𝐨𝐠𝐬 <𝐩𝐨𝐝> The go-to for application-level debugging. Perfect for spotting exceptions, crashes, or misconfigurations. 🔹 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐞𝐱𝐞𝐜 -𝐢𝐭 <𝐩𝐨𝐝> -- 𝐛𝐚𝐬𝐡 Jump inside the pod to inspect the environment, test connectivity, or run manual checks. 🔹 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐠𝐞𝐭 𝐞𝐯𝐞𝐧𝐭𝐬 Shows what’s happening at the cluster level - scheduling errors, image pull failures, or permission issues. 𝗣𝗿𝗼 𝘁𝗶𝗽: When something breaks at 2 AM, this simple set of commands is often all you need to get to the bottom of it. 👉 Save this post for your next Kubernetes firefight. 🔹 Follow Rakshita Belwal for more DevOps lessons, tips, and real-world stories. #DevOps #k8s #careerbytecode #rakshitabelwal

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scenario-based DevOps interview questions & answers 16. What if a Terraform apply fails halfway? Answer: Terraform creates a partial state → Fix the error → Run terraform plan again → Apply → Or use terraform refresh to sync state. 17. How do you manage multi-cloud deployments (GCP + Azure)? Answer: Use Terraform with multiple providers → Create modules for each cloud → Keep separate state files for GCP and Azure. 18. How do you handle a disk full issue in Linux? Answer: Run df -h to check usage → Clear logs from /var/log → Remove unused Docker images/containers → Expand disk if required. 19. How do you implement rollback in Terraform? Answer: Use version control to revert .tf files → Run terraform apply again → Restore previous infra state from remote backend version. 20. How do you ensure security in DevOps pipelines? Answer: Scan code with SonarQube Scan images with Trivy/Anchore Use IAM least privilege in GCP/Azure Store secrets in Secret Manager/Key Vault Enable audit logging 21. How do you manage Terraform modules for reusability? Answer: Create modules for common components (VPC, VM, AKS/GKE, IAM) → Store them in Git → Call modules in different projects with version control. 22. What if two team members run Terraform apply at the same time? Answer: Use remote backend with state locking (like Azure Blob with locking or GCS with locking) → This prevents conflicts. 23. How do you perform Canary Deployment in Kubernetes? Answer: Deploy a new version to a small % of users → Use Istio/NGINX Ingress for traffic routing → Gradually increase traffic → Rollback if errors. 24. How do you troubleshoot “ImagePullBackOff” in Kubernetes? Answer: Check if image exists in registry. Validate credentials/secret for private registry. Verify image tag. Fix and redeploy. 25. How do you integrate Jenkins with GitHub? Answer: Configure GitHub webhook → Connect Jenkins job to repo → Trigger builds automatically on code push/PR. 26. How do you perform zero-downtime DB migration in CI/CD? Answer: Use Liquibase/Flyway migration scripts → Apply backward-compatible schema changes → Deploy app → Apply destructive changes only later. 27. What if a Jenkins agent node goes offline? Answer: Check agent logs → Restart service → Verify connectivity with master → Add auto-scaling slaves (Kubernetes or cloud VMs). 28. How do you set resource limits in Kubernetes? Answer: Define requests & limits in pod spec → Ensures fair resource allocation and prevents pod from consuming all CPU/memory. 29. How do you implement CI/CD for Terraform? Answer: Use Jenkins/GitHub Actions → terraform fmt, terraform validate, terraform plan, approval stage, then terraform apply. Store state in backend. 30. How do you monitor logs in Kubernetes? Answer: Use kubectl logs for quick debugging → For centralized logging, use EFK (Elasticsearch + Fluentd + Kibana) or Loki + Grafana.

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One of the fundamental concepts in Kubernetes is Pod Graceful Shutdown When a pod shuts down in Kubernetes, It sends a SIGTERM signal to the container. If your app does not handle SIGTERM properly, it might stop in the middle of serving a user request, saving data or processing a file etc You can write custom logic in your application code to handle the SIGTERM signal. Also, you can slow down your apps shutdown a bit using a preStop hook. This hook runs before Kubernetes sends the SIGTERM, giving your app a buffer to clean up or finish tasks. Last week, we sent out a detailed newsletter covering: - What are SIGTERM and SIGKILL - How Kubernetes handles SIGTERM and SIGKILL - How applications can handle SIGTERM for graceful shutdown - The role of PreStop hooks and why it matters - The link between SIGTERM and container PID 1 → 𝗥𝗲𝗮𝗱 𝗶𝘁 𝗛𝗲𝗿𝗲 (𝟭𝟬𝟬% 𝗳𝗿𝗲𝗲): https://bit.ly/dcube-nl PS. How are you handling graceful pod shutdown in your apps? #devops #kubernetes

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𝐖𝐡𝐚𝐭 𝐈𝐬 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐊𝐚𝐫𝐩𝐞𝐧𝐭𝐞𝐫 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐓𝐞𝐦𝐩𝐥𝐚𝐭𝐞 ❓ Kubernetes Karpenter is a node lifecycle management solution that provides just-in-time compute provisioning for Kubernetes clusters. Unlike traditional cluster autoscalers that work with predefined node groups, Karpenter provisions nodes directly based on pod scheduling requirements and resource constraints. This Architecture is ideal for DevOps engineers managing Kubernetes clusters, developers handling traffic surges, cloud teams optimizing costs, and EKS teams implementing modern autoscaling, especially when dealing with pending pods, over-provisioned clusters, or seeking cost reduction without performance trade-offs. The Kubernetes Karpenter Architecture template highlights these main components 👇🏻 ☑️ 𝗣𝗲𝗻𝗱𝗶𝗻𝗴 𝗣𝗼𝗱𝘀: Pods waiting for a room on a node. ☑️ 𝗦𝗰𝗵𝗲𝗱: Built-in scheduler of Kubernetes. ☑️ 𝗞𝗮𝗿𝗽𝗲𝗻𝘁𝗲𝗿 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿: Watch for pending pods and construct new nodes as necessary. ☑️ 𝗘𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆: Nodes already operating in your cluster. ☑️ 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆: Right-sized nodes, Karpenter adds automatically. ☑️ 𝗕𝘂𝗿𝘀𝘁𝗮𝗯𝗹𝗲 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆: Additional nodes for brief high traffic. ☑️ 𝗡𝗼𝗱𝗲 𝗚𝗿𝗼𝘂𝗽𝘀: Groups of related nodes in your cluster. ☑️ 𝗪𝗼𝗿𝗸𝗹𝗼𝗮𝗱 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Karpenter's technique of balancing demand with resources. ☑️ 𝗖𝗹𝘂𝘀𝘁𝗲𝗿 𝗔𝘂𝘁𝗼𝘀𝗰𝗮𝗹𝗲𝗿: Older autoscaling tool; Karpenter often replaces it. ☑️ 𝗦𝗽𝗼𝘁 𝗜𝗻𝘀𝘁𝗮𝗻𝗰𝗲𝘀: Less expensive instances employed as needed. ☑️ 𝗜𝗻𝘀𝘁𝗮𝗻𝗰𝗲 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Automatically drains and deletes idle or expired nodes. ☑️ 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Avoids over-provisioning and waste. ☑️ 𝗙𝗮𝘂𝗹𝘁 𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲 𝗠𝗲𝗰𝗵𝗮𝗻𝗶𝘀𝗺: Keeps your application running even if some nodes fail. Visualise this end‑to‑end flow in seconds with the Cloudairy Kubernetes Karpenter Architecture Template - https://lnkd.in/dcxxhk9x #Cloudairy #Cloudchart #TechnicalDiagrams #Kubernetes #Karpenter #Architecture #Templates #KubernetesArchitecture #CloudComputing #DevOps #Containerization #Microservices 

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This HPA config change can make a huge difference in scaling. In Kubernetes, HPA waits for at least 10% change in CPU/memory usage before adding or removing pods. You cant control how sensitive it was to increase or decrease in usage. For example, if you set 70% as the usage target, Kubernetes would only scale up if it went above 77%, and scale down if it went below 63%. The new alpha feature has a solution for this. Read our DevOps tips to know more. 𝗛𝗣𝗔 𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲 𝗟𝗲𝘃𝗲𝗹𝘀: https://lnkd.in/gmMbKgmh ♻️ PS: Repost and share it with the DevOps community. #devops #kubernetes

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You’d be surprised → Many engineers work with GitOps and Argo CD daily, but when asked “How would you deploy and manage apps across multiple Kubernetes EKS clusters from a single control plane?” …most get stuck. That’s why I broke down a hands-on project where I implemented a Hub-and-Spoke GitOps model using Argo CD on Amazon Web Services (AWS) EKS 👇 • Hub Cluster → runs Argo CD and acts as the central control plane • Spoke Clusters → two independent EKS clusters receiving app deployments • Argo CD Applications & ApplicationSets → define once, deploy seamlessly to multiple clusters • Accessing ArgoCD UI → exposed via NodePort for simplicity in the demo • Demo App → Guestbook deployed simultaneously to Spoke 1 & Spoke 2 using GitOps The best part? → With this setup, you manage all workloads for multiple clusters from one central hub, while keeping everything declarative and version-controlled in Git. This kind of multi-cluster GitOps workflow is critical for: → Enterprises running apps across regions → DR & high availability strategies → Centralized governance and compliance If you’re exploring Argo CD beyond single-cluster demos, this hub-and-spoke pattern is a must-try. ♻️ If this sparked ideas, share or repost with your network → it could help someone stuck on their multi-cluster GitOps journey. Follow for more → Amay Jaiswal #Kubernetes · #GitOps · #ArgoCD · #DevOps · #CloudNative · #EKS · #AWS · #MultiCluster · #InfrastructureAsCode · #SRE · #DevOpsCommunity

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🚨 Here’s how Terraform works, component by component: Let’s break it down in plain English. 1. Terraform CLI (You) The moment you type terraform apply, the CLI takes over. It reads your .tf files and parses them into an execution plan. But here’s the catch: the CLI itself is stateless. It depends on something else for memory. If your CLI crashes, your infra isn’t lost. The state decides what happens next. 2. Providers (AWS, Azure, GCP, etc.) Providers are Terraform’s drivers. Each API call you write (aws_instance, google_storage_bucket) maps to a provider plugin. Terraform dynamically downloads these when you run init. If the provider breaks (bad version, API drift), apply dies before creating resources. 3. State File (terraform.tfstate) This is Terraform’s memory. It stores what infra exists, what’s new, and what’s drifted. Local vs Remote → remote (S3, GCS, etc.) is safer, but adds complexity. Corrupted state equals chaos. Terraform has no clue what exists. (Pro tip: never edit this manually unless you enjoy pain.) 4. Execution Plan Terraform calculates the desired state vs the current state. It figures out a “diff” of which resources to create, update, or destroy. It then shows you the summary: “Plan: 2 to add, 1 to change, 0 to destroy.” If your plan is wrong, your infra will be wrong. Garbage in means garbage out. 5. Resource Creation Terraform applies the plan and calls provider APIs in the correct order. It uses a dependency graph so that, for example, VPCs are created before EC2 instances. It parallelizes wherever possible to save time. If one resource fails (for example, IAM role not found), Terraform halts. This is where half-applied infra nightmares start. 6. State Update After resources are created, updated, or destroyed, Terraform refreshes the state. If this step fails (network outage, remote lock issue), your infra exists, but the state is stale. This is why rollback is painful. Infra reality does not equal Terraform memory. Key Insight: Terraform isn’t just “infra as code.” It is a delicate dance between: CLI (orchestrator), Providers (drivers), State (memory), Execution Plan (diff), Resource Actions (API calls) If you don’t understand each step, you won’t survive a broken apply. At InfraThrone, we don’t just teach Terraform commands. We train you on broken state recovery, half-applied infra debugging, and real-world chaos in production. Next Cohort Starts Nov 1 Few Seats Left → infrathrone.xyz #DevOps #Cloud #SRE #Terraform #ProductionChaos #Infrathrone

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🚀 Want to understand how Terraform works in one image? Here’s a simple yet powerful breakdown! 💡 Terraform by HashiCorp is a game-changer in modern DevOps and cloud automation. It takes in your templates, scripts, and policies — and turns them into full-blown infrastructure covering: ✅ Applications ✅ Storage ✅ Cloud Infrastructure ✅ Networking ✅ Security 🔧 Write once, deploy anywhere. 📦 Automate infrastructure like a pro. 🌍 Scale with confidence. If you're a DevOps enthusiast, Cloud engineer, or just starting your IaC (Infrastructure as Code) journey — this is your cue to dive deep into Terraform! 👇 What part of Terraform excites you the most? Let’s discuss in the comments. #Terraform #DevOps #CloudComputing #InfrastructureAsCode #HashiCorp #AWS #Azure #GoogleCloud #IaC #Automation #CloudEngineering #DevOpsCommunity #TechCareers #CloudNative #SRE #DevSecOps #Kubernetes #LinkedInTech #TechCommunity #CloudSkills #LearnTerraform

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🛑 Stop mixing up Terraform and Ansible — they’re not the same! Terraform builds & Ansible customizes. Let’s clear this up with a simple analogy live on Zoom ✅ https://lnkd.in/dNYrJHvU 👷‍♂️ Terraform = The Construction Crew Think of Terraform as the team that shows up first on an empty site. They pour the foundation, build the walls, install the main plumbing and electrical wiring — basically, they make sure the entire structure stands solid. 👉 With Terraform you can: Set up networks (VPCs, subnets, firewall rules) Launch servers/VMs (EC2, Azure VM, GCP VM, Kubernetes clusters) Create storage (S3 buckets, persistent disks) Spin up databases (RDS, CloudSQL, etc.) Add load balancers to distribute traffic 🎨 Ansible = The Interior Designers Once the building is ready, Ansible comes in. They paint the walls, bring in furniture, set up the lighting, and hang the curtains — making the place functional, customized, and comfortable. 👉 With Ansible you can: Configure systems (install packages like Docker, Nginx, Git…) Deploy applications (clone repos, run apps) Set up services (configure Nginx, PostgreSQL, etc.) Manage security (firewall rules, SSH hardening) Automate maintenance (restart services, manage configs, apply templates) 💡 Quick Note: Yes, there’s a bit of overlap — Terraform can do some basic configuration, and Ansible can deploy resources. But… Terraform excels at creating and managing infrastructure. Ansible shines at configuration, customization, and ongoing maintenance.

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