How to Automate Kubernetes Stack Deployment

🚀 Deploying a "Production-Grade", Secure, and High-Availability Kubernetes Cluster with Ansible As a Platform Engineer, moving from a simple lab cluster to infrastructure that is truly ready for production is a major challenge. I wanted to automate the deployment of a robust architecture that meets security standards (CIS Hardened) while delivering top-tier performance. I moved beyond standard kubeadm to the next level with **RKE2** and **Cilium**. Using Ansible, I fully automated: 🔹 **HA Architecture**: 3 Control-Plane nodes (embedded etcd) + Workers. 🔹 **Advanced Networking**: Cilium CNI replacing kube-proxy with **eBPF** (maximum performance). 🔹 **Security "By Design"**: RKE2 (FIPS/CIS compliant) with hardened configuration. 🔹 **Dual-Stack**: Full native IPv4 and IPv6 support. 🔹 **Ingress & Services**: Proper Load Balancing configuration. 💡 **Why is this stack a game changer?** ✅ **Security**: RKE2 is built for critical environments (Government/Banking). ✅ **Performance**: Using eBPF via Cilium removes the iptables overhead. ✅ **Reproducibility**: A single Ansible command to go from bare metal to a fully operational cluster. ✅ **Modernity**: A future-proof stack with IPv6 support and Hubble observability. This is the perfect blueprint for spinning up iso-functional staging or production environments in minutes. 📂 full documentation are on GitHub: https://lnkd.in/ecrT9KRk 📂 playbooks https://lnkd.in/eCC28dwH 👇 If you are still using kubeadm or considering switching to RKE2, let me know your thoughts in the comments! #Kubernetes #RKE2 #Ansible #Cilium #eBPF #DevOps #PlatformEngineering #InfrastructureAsCode #Security #IPv6 #HACluster

End-to-End Cloud DevOps Pipeline I’m thrilled to share my Cloud DevOps Project, where I designed and automated a complete CI/CD pipeline that integrates cloud infrastructure, Kubernetes, and modern DevOps tools simulating a real-world production environment from scratch. This project helped me bring together everything I’ve learned in DevOps, Cloud, and Automation showing how CI/CD pipelines can be built in a hybrid environment using GitOps best practices. Key Highlights: 🔹 Hybrid Setup – Built an AWS EKS Cluster with dedicated node groups, ensuring isolation between application and database workloads using taints, tolerations, and node affinity for efficient and secure scheduling. 🔹 Infrastructure as Code – Provisioned AWS VPC, EC2, IAM, and S3 with Terraform Modules and remote backend (S3 + DynamoDB). 🔹 Configuration Management – Automated EC2 setup with Ansible Dynamic Inventory and reusable roles. 🔹 Continuous Integration (CI) with Jenkins – Pipeline stages: ✔️ Build Docker Image ✔️ Security Scan with Trivy ✔️ Push to DockerHub ✔️ Auto-update Kubernetes Manifests & commit changes to Git 🔹 Continuous Deployment (CD) with ArgoCD – Automatically syncs updated manifests from GitHub to the Kubernetes cluster. 🔹 Monitoring & Observability – Prometheus + Grafana with custom dashboards and alerts. Tech Stack: Terraform · Ansible · Jenkins (CI) · Docker · Kubernetes · ArgoCD (CD) · Trivy · Tailscale · Prometheus · Grafana · AWS Full Project & Code: https://lnkd.in/d6TBJTa2 Looking forward to building more cloud-native and production-ready DevOps solutions #DevOps #CloudDevOps #CI #CD #GitOps #Terraform #Kubernetes #Jenkins #Ansible #Docker #Prometheus #Grafana #InfrastructureAsCode #Tailscale #CloudNative

Running Kubernetes shouldn't require a dedicated platform team Amazon EKS Auto Mode fundamentally changes how we think about Kubernetes infrastructure management. Instead of spending weeks configuring autoscaling, load balancers, and storage drivers, AWS now manages the entire data plane for you. Who is this for? Teams that want production-grade Kubernetes without deep EKS expertise. If you're spending more time managing cluster infrastructure than shipping applications, Auto Mode is built for you. What makes this different from traditional EKS? Traditional EKS requires you to set up and maintain Managed Node Groups, install Karpenter or Cluster Autoscaler, configure the AWS Load Balancer Controller, deploy EBS CSI drivers, and manage Pod Identity agents. Each component needs ongoing maintenance, upgrades, and troubleshooting. EKS Auto Mode eliminates all of that. AWS manages everything as core components, not add-ons. The operational burden reduction is real: Before Auto Mode: Install 6+ add-ons, configure node groups, manage AMI updates, patch security vulnerabilities, handle node lifecycle, troubleshoot autoscaling issues. With Auto Mode: Deploy cluster, run workloads. That's it. The architecture is built on proven technology: • Karpenter-based autoscaling (fully managed by AWS) • Bottlerocket AMIs with pre-installed drivers • Automatic pod-driven scaling without node group configuration • Built-in load balancer controllers for ALB/NLB • Integrated Pod Identity and EBS CSI drivers Security is hardened by default: • Immutable AMIs with SELinux enforcing mode • Read-only root filesystems • No SSH or SSM access to nodes • Automatic node replacement every 21 days (configurable) • Automated security patches and OS upgrades Two pre-configured NodePools handle most workloads: 1. general-purpose: C/M/R instance families, Gen 4, AMD, On-Demand 2. system: ARM/AMD support, tainted for critical EKS add-ons You can add custom NodePools for specific requirements like GPU workloads, Spot instances, or workload isolation. Can you migrate existing clusters? Yes. EKS Auto Mode works with both new and existing clusters. You can even mix Auto Mode-managed nodes with self-managed nodes in the same cluster. After enabling Auto Mode, simply uninstall the components it now manages (like Karpenter or AWS Load Balancer Controller). Ready to try it? Start with this hands-on workshop: https://lnkd.in/dNBZTD8F Official documentation: https://lnkd.in/dTiVaBtb Best practices guide: https://lnkd.in/dPHWQMGK Image credit: https://lnkd.in/dcCPcN9b #AWS #EKS #EKSAutoMode #Kubernetes #CloudNative #DevOps #Infrastructure

🚀 End-to-End DevOps Project on AWS I recently completed a cloud-native DevOps project where I built and deployed a full-stack application using Terraform, Jenkins, Docker and Kubernetes on AWS. 🔗 GitHub Repo: 👉 https://lnkd.in/g7G2Cd-v Here’s a breakdown of what I implemented: 🏗️ 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗖𝗼𝗱𝗲 – 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺 • Used Terraform to automate infrastructure provisioning with state management and locking enabled through AWS S3. ✅ Resources created: • VPC with 3 subnets: • Public Subnet → Bastion Host, VPN, ALB (Ingress Controller) • Private Subnet → EKS Cluster • DB Subnet → RDS (MySQL) • Integrated with Route53 (DNS), CDN, and EFS for persistent storage. ☸️ 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 – 𝗘𝗞𝗦 • Traffic enters through AWS ALB, handled by Ingress Controller • Routed to microservices via Kubernetes Services • Used Deployments, ConfigMaps, and Helm for management • Persistent data handled using EFS volumes via PVCs • Followed clean microservices architecture for separation of concerns 🚀 𝗖𝗜/𝗖𝗗 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 – 𝗝𝗲𝗻𝗸𝗶𝗻𝘀 • Set up a complete CI/CD pipeline triggered by GitHub webhooks. Jenkins pipeline includes: 1. Dependency installation 2. Code analysis with SonarQube 3. Infra provisioning using Terraform 4. Docker image build & push to Amazon ECR 5. Kubernetes deployment using Helm 📌 This project helped me understand the real-world DevOps workflow, from infrastructure setup to CI/CD automation and scalable deployments on EKS. 🔗 GitHub Repo: 👉 https://lnkd.in/g7G2Cd-v 🔁 Repost if you found it useful #AWS #DevOps #Terraform #Jenkins #EKS #Kubernetes #CICD #CloudComputing #InfrastructureAsCode #Helm #SonarQube #ECR #EFS #Route53 

𝐈𝐟 𝐘𝐨𝐮’𝐫𝐞 𝐒𝐭𝐢𝐥𝐥 𝐈𝐧𝐬𝐭𝐚𝐥𝐥𝐢𝐧𝐠 𝐊𝟖𝐬 𝐓𝐨𝐨𝐥𝐬 𝐌𝐚𝐧𝐮𝐚𝐥𝐥𝐲, 𝐘𝐨𝐮’𝐫𝐞 𝐃𝐨𝐢𝐧𝐠 𝐃𝐞𝐯𝐎𝐩𝐬 𝐖𝐫𝐨𝐧𝐠 Many teams still install Kubernetes tools manually and then complain about inconsistent environments, broken setups, or configurations lost in the air. I’ve been there too. Helm install here, kubectl apply there, and every cluster ends up looking different. So I decided to fix this for myself and for anyone who wants a clean, automated workflow. I built a fully automated EKS setup using Terraform where ArgoCD, Prometheus, Grafana and the AWS Load Balancer Controller are deployed with zero manual steps. Everything is repeatable and production-friendly. One plan, one apply, and your whole stack is ready. Watch the video https://lnkd.in/dDiPUYSb Source code repo https://lnkd.in/dxXgQRES If this helps you, please feel free to use or improve it. Happy learning. Happy Learning Aman Pathak #DevOps #Kubernetes #AWS #Terraform #ArgoCD #GitOps #Monitoring

Everybody says not to start with Kubernetes but to paraphrase Kelsey Hightower, "Kubernetes is the new Linux", so it's important to know how to build on it. Here's how I would build a future-proof Kubernetes cluster on AWS and GitHub that will allow you to iterate quickly and scale up when you need to. I like to think of Kubernetes-based infrastructure in three layers. They need to be deployed in this order: 1) Physical infrastructure layer (a set of nodes running Kubernetes) 2) Application infrastructure layer (the services you need to run your apps, things like: cert managers, ingress controllers, etc) 3) Application layer (your actual apps you want to run) Here's the high-level recipe: 1) Start by deploying AWS EKS and its requisite resources with Terraform. This should be a single Terraform module in a GitHub repo with a GitHub Action for automatically running `terraform apply` when you push a commit to main. 2) Use Terraform to bootstrap FluxCD on your cluster to allow you to run "GitOps". This can be in the same module as the above just add the right dependencies. 3) Store your application infrastructure services as Helm charts in a GitHub repo. Use FluxCD HelmRelease resources to automatically sync these charts to your cluster (this is the GitOps part). This should include things like AWS External Secrets Operator, Nginx Ingress Controller, etc. 4) Containerize your app components and add GitHub Actions to their repos to build and publish images to ECR when you commit to main. 5) Write Helm charts for each of your app's components (frontend, backend, queue, DB, etc) and add HelmRelease resources for them. 6) Add ImageUpdateAutomation resources to scan ECR and update your Helm chart values for each of your app components. This will trigger a deployment from the HelmRelease setup we did in step 5. This can be a lot of work and there are lots of details but the end result is: - To deploy your latest code to the cluster all you have to do is commit to the main branch. - To update a Helm chart all you have to do is commit to the main branch and bump the version tag in the HelmRelease resource. - To scale up your apps just edit one value in their HelmRelease. - To scale up your cluster just change one number in the Terraform values. - To debug your system just use standard `kubectl` commands that AI knows well. - All of this infrastructure and configuration is well versioned and centralized in a single repo so you can add branch protections and prevent drift. This might be overkill for your vibecoded todo app but for any system requiring serious infrastructure considerations I'd argue its better to pay this cost upfront before your customers come knocking about downtime and latency issues later.

🚀 Deploying Kubernetes workloads with Amazon EKS and Helm — simplified! When managing modern microservices in the cloud, reproducibility, modularity, and automation are everything. That’s why I use Helm charts to package, version, and deploy Kubernetes resources onto Amazon EKS. Here’s how I structure my production deployments: ✅ Source Control (Git/SVN) stores all Kubernetes YAMLs and Helm charts ✅ Helm (from CI/CD or local client) interacts with the EKS control plane via the Kubernetes API Server ✅ kubectl supports real-time inspection, rollouts, and config updates ✅ Charts can be hosted on Amazon S3, or optionally on any remote Helm repo (like GitHub, JFrog, ArtifactHub) ✅ The EKS data plane brings workloads to life — Pods, Services, and Ingress objects run across managed worker nodes 🎯 Why I love this approach: Fully automated and reproducible deployments Centralized version control of infrastructure Seamless management of dev, test, and prod environments I'm running this exact flow in production, and the results speak for themselves — speed, security, and simplicity. 💬 Got questions about Helm, CI/CD integration, or production-ready EKS deployments? I’d love to chat and learn from your approach too. 👇 Follow me for more practical DevOps insights, architecture breakdowns, and real-world cloud engineering tips! #Kubernetes #AWS #EKS #HelmCharts #CloudNative #DevOps #CI_CD #InfrastructureAsCode #GitOps #CloudEngineering #TechDiagrams #PlatformEngineering #OpenSource #IaC #Microservices #LearningInPublic #FollowMeForMore

End-to-End CI/CD Pipeline for Kubernetes Deployment This project demonstrates a complete, secure, and automated CI/CD workflow for deploying applications on Kubernetes using modern DevOps tools and GitOps practices. 🔧 Terraform Infrastructure as Code (IaC) for provisioning and managing cloud resources. 🤖 Jenkins Automates build, test, and deployment pipelines. 🛠️ CI/CD Pipeline Includes ✅ Code quality analysis ✅ Dependency vulnerability scanning ✅ File system security scans ✅ Docker image build 🔍 Trivy Scans Docker images for vulnerabilities before pushing them to the registry. 📦 Amazon ECR Stores and manages Docker images securely. 🌍 GitHub Source control and GitOps repository for deployment manifests. 🚀 Argo CD Automates Kubernetes deployments using a declarative GitOps approach. 🌐 Application Load Balancer (ALB) Distributes incoming traffic efficiently across services. 🌐 GoDaddy (DNS & Domain Management) Handles domain and DNS configuration. 🎛️ Application Architecture Frontend, Backend, and Database deployed as separate Kubernetes pods Secure secrets management for ECR and database access 📊 Monitoring & Observability 📈 Prometheus for metrics collection 📊 Grafana for visualization and insights This CI/CD pipeline ensures scalability, security, and reliability for cloud-native applications running on Kubernetes. #DevOps #Kubernetes #CICD #Terraform #Jenkins #ArgoCD #AWS #GitOps #CloudNative