Shaikh Imran’s Post

🟢 𝐘𝐀𝐌𝐋 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 📄 A clean and practical reference to understand YAML essentials at a glance: ● Scalar types (int, float, string, boolean, date) ● Variables & anchors ● Multiline and folded strings ● Sequences & mappings ● Comments and structure ● Core types and special keys Perfect for DevOps, Kubernetes, and configuration-heavy workflows. #YAML #DevOps #Kubernetes #Config #Cloud #Docker #Infrastructure #Programming #Tech #Learning

Sharing a quick Docker Cheat Sheet that helps in day-to-day container operations. This is something I personally refer to while working on real-time projects and CI/CD pipelines. 🔹 Basic Docker commands (build, run, stop, remove) 🔹 Image & container management 🔹 Dockerfile key instructions 🔹 Volume & network basics 🔹 Debugging container issues In modern DevOps workflows, Docker plays a major role in packaging applications and ensuring consistency across environments — from development to production. This cheat sheet is useful for: ✅ Beginners learning containerization ✅ DevOps engineers preparing for interviews ✅ Developers working with microservices Save it, use it, and simplify your container journey 🚀 #Docker #DevOps #Containers #CICD #Kubernetes #Cloud #AWS #Learning #Tech

Starting my DevOps journey. I’ve begun learning DevOps to strengthen my understanding of modern backend systems and deployment practices. Currently following a structured learning resource by Faizal Menon that covers core concepts like CI/CD, containerization (Docker), and infrastructure basics. Here is the resource I’m learning from: https://lnkd.in/gSgjtVZt My goal is to build hands-on projects and apply these concepts in real-world scenarios. If you have any suggestions or beginner-friendly resources, I would appreciate your guidance. #DevOps #LearningJourney #BackendDevelopment #ContinuousLearning

🚀 𝗥𝗲𝗮𝗹 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗜𝘀𝘀𝘂𝗲𝘀 𝗜 𝗙𝗮𝗰𝗲𝗱 𝗗𝘂𝗿𝗶𝗻𝗴 𝗙𝗶𝗿𝘀𝘁-𝗧𝗶𝗺𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 (𝗪𝗶𝘁𝗵 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀) During initial Kubernetes deployments, I faced several real-world issues. Sharing some common scenarios along with the commands I used to troubleshoot: 🔴 1. 𝗖𝗿𝗮𝘀𝗵𝗟𝗼𝗼𝗽𝗕𝗮𝗰𝗸𝗢𝗳𝗳 📌 Issue: Pod was continuously restarting 🔍 Troubleshooting: kubectl get pods kubectl logs <pod-name> kubectl describe pod <pod-name> 🛠️ Fix: Identified missing environment variable Updated deployment YAML kubectl apply -f deployment.yaml 🔴 2. 𝗜𝗺𝗮𝗴𝗲𝗣𝘂𝗹𝗹𝗕𝗮𝗰𝗸𝗢𝗳𝗳 📌 Issue: Pod failed to pull image 🔍 Troubleshooting: kubectl describe pod <pod-name> 🛠️ Fix: Verified image name/tag Configured imagePullSecret for private registry 🔴 3. 𝗣𝗼𝗱 𝗶𝗻 𝗣𝗲𝗻𝗱𝗶𝗻𝗴 𝗦𝘁𝗮𝘁𝗲 📌 Issue: Pod stuck in Pending 🔍 Troubleshooting: kubectl get pods kubectl describe pod <pod-name> 🛠️ Fix: Checked node resources Adjusted CPU/Memory requests in YAML 🔴 4. 𝗖𝗼𝗻𝗳𝗶𝗴𝗠𝗮𝗽 / 𝗦𝗲𝗰𝗿𝗲𝘁 𝗜𝘀𝘀𝘂𝗲𝘀 📌 Issue: Application failed to start 🔍 Troubleshooting: kubectl get configmap kubectl get secrets kubectl describe pod <pod-name> 🛠️ Fix: Corrected ConfigMap/Secret reference in deployment 🔴 5. 𝗟𝗶𝘃𝗲𝗻𝗲𝘀𝘀 / 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗯𝗲 𝗙𝗮𝗶𝗹𝘂𝗿𝗲 📌 Issue: Pod restarting repeatedly 🔍 Troubleshooting: kubectl describe pod <pod-name> kubectl logs <pod-name> 🛠️ Fix: Updated probe configuration Increased initialDelaySeconds 💡 Key Learning: Most Kubernetes deployment issues are related to configuration, image, or resource allocation. A structured troubleshooting approach using kubectl commands helps resolve issues quickly. 👉 Real DevOps work = Troubleshooting + Automation #Kubernetes #DevOps #Troubleshooting #CloudNative #Docker #CICD #Learning

🚀 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 𝐭𝐡𝐚𝐭 𝐞𝐯𝐞𝐫𝐲 𝐃𝐞𝐯𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐦𝐮𝐬𝐭 𝐬𝐚𝐯𝐞 If you're working with Kubernetes, this is your daily survival kit 👇 💡 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐠𝐞𝐭 𝐢𝐧 𝐨𝐧𝐞 𝐩𝐥𝐚𝐜𝐞: ⚙️ 𝐂𝐥𝐮𝐬𝐭𝐞𝐫 & 𝐂𝐨𝐧𝐟𝐢𝐠 → kubectl version, cluster-info, contexts 📦 𝐏𝐨𝐝𝐬 & 𝐋𝐨𝐠𝐬 → get pods, describe, logs, exec 🚀 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬 → apply, scale, rollout, restart 🌐 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬 & 𝐈𝐧𝐠𝐫𝐞𝐬𝐬 → ClusterIP, NodePort, LoadBalancer 🗂️ 𝐂𝐨𝐧𝐟𝐢𝐠𝐌𝐚𝐩𝐬 & 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 → manage configs securely 📊 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐇𝐏𝐀 → autoscaling, metrics, events 🧠 𝐓𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭𝐢𝐧𝐠 → CrashLoopBackOff, ImagePull errors 🔥 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬? Because Kubernetes is powerful… but also complex. And having this cheat sheet = faster debugging + better deployments 💬 𝐓𝐢𝐩: Don’t just read commands ❌ 👉 Practice them 👉 Break things 👉 Fix them That’s how real DevOps engineers grow 🚀 📌 Save this post if you’re learning DevOps / Cloud / Kubernetes Follow me for more practical tech content 👇 #Kubernetes #DevOps #CloudComputing #Docker #SoftwareEngineering #TechCareers #Learning #Programming #AI #CareerGrowth

Ever deployed a Docker image… and waited forever for it to pull? Your app isn’t slow. 👉 Your image is just bloated. Most developers focus on writing code. But in DevOps, how you ship matters just as much as what you build. A poorly optimized Docker image can: ❌ Slow down deployments ❌ Increase cloud costs ❌ Expand your attack surface ❌ Kill CI/CD performance Here’s how top engineers optimize their Docker images: 🔹 Use lightweight base images (Alpine / Distroless) 🔹 Apply multi-stage builds to remove unnecessary tools 🔹 Reduce layers & clean caches 🔹 Use .dockerignore to avoid useless files 🔹 Install only production dependencies 🔹 Run containers as non-root for security 💡 Real impact: Optimizing a Docker image can reduce size by 70%+ → Faster builds → Faster deploys → More secure systems In modern cloud environments, efficiency = performance + cost + security And Docker optimization sits right at that intersection. 🔥 If you're learning DevOps, this is not optional anymore. 💬 What’s the biggest Docker image you’ve ever built? #Docker #DevOps #CloudComputing #Kubernetes #SoftwareEngineering #BackendDevelopment #Microservices #CloudNative #TechTips #Programming #IT #Learning #CI_CD

💡 Understanding DevOps Flow (Simple Breakdown) When I started learning DevOps, everything felt confusing. Now I see it like this: 👨💻 Developer writes code ⬇️ 📁 Code pushed to GitHub ⬇️ 🤖 Jenkins builds & deploys ⬇️ 📦 Docker runs the application in containers ⬇️ ☸️ Kubernetes manages containers ⬇️ ☁️ AWS provides the infrastructure 🔁 All of this is connected through a CI/CD pipeline. The biggest realization for me: DevOps is not about learning tools separately — it’s about understanding how they work together. Still learning, but things are starting to make sense 🔥 #DevOps #CI_CD #Docker #Kubernetes #AWS #LearningInPublic

If you are interested in DevOps, learning Kubernetes is an absolute game-changer. I have just put together a quick guide breaking down how Kubernetes orchestrates containerized applications—much like a master conductor leading an orchestra! 🎼 In this guide, we cover: Local Setup: Getting started quickly using Minikube for a local testing environment. YAML Configs: Ditching long command lines and managing your deployments with simple, clean YAML files. High Availability & Scaling: Learning how to automatically scale your app to handle heavy traffic and ensure zero downtime. Data Safety: Using Persistent Volumes so your database information is never lost, even if a server or pod crashes. Check out the attached document to see practical examples of deploying a multi-container app! Let me know in the comments: what is your favorite Kubernetes feature? 👇 #Kubernetes #DevOps #Docker #Microservices #TechCareers #CloudComputing

I’ve been exploring containerization and now have a solid understanding of how Docker works. 🔹 Key concepts I learned: Containers vs Images Basic Docker commands Writing Dockerfiles Building and running containers 🧠 Quick understanding: Docker allows us to package an application along with its dependencies into a container, so it runs consistently across any environment. 📦 Dockerfile helps automate the process of creating Docker images. 🔽 Simple workflow diagram: Code (App Files) ↓ Dockerfile (Instructions) ↓ Docker Image (Build) ↓ Docker Container (Run) ↓ Application Running 🚀 #Docker #DevOps #CloudComputing #Learning #AWS #BeginnerJourney

kubectl apply Explained | Kubernetes Workflow Made Simple @skillraiz Confused about what happens after running kubectl apply? 🤔 Let’s break it down into a simple, easy-to-understand flow so you can work with Kubernetes confidently 💻 🚀 Behind the Scenes When you run the command, the request goes to the Kubernetes API Server – the entry point of the cluster 🌐 It validates your YAML file and checks permissions using RBAC 🔐 Once approved, the desired state is stored in ETCD, the cluster database 📦 Then the Controller Manager ensures Pods and ReplicaSets are created as needed ⚙️ ☁️ From Scheduling to Execution The Scheduler assigns Pods to the best nodes based on resources and rules 🧠 Then the Kubelet pulls container images and runs them on worker nodes 🐳 Finally, Kubelet continuously updates the Pod status back to the API Server 📊 💡 Understanding this workflow helps you debug faster and build real DevOps skills 🎯 Learn Kubernetes, AWS & DevOps with hands-on training @Skillraiz 👉 Join our FREE Demo Today 📞 +91 8106109054 🌐 www.skillraiz.com 💻 Learn • Practice • Get Placed #Skillraiz #Kubernetes #Kubectl #DevOps #CloudComputing #K8s #onlinetraining #onlinecoaching #trendingréels #trendingvideos #day1