Bhanu Teja’s Post

🚀 Stop wasting time on Docker CLI chaos — meet LazyDocker If you work with Docker daily, you already know the pain: Long container IDs Endless docker ps, logs, exec commands Constant tab switching just to debug something simple I recently started using LazyDocker, and it completely changed how I interact with containers. 🔥 What is LazyDocker? It’s a terminal UI for Docker and Docker Compose that gives you a clean, interactive view of: Containers Images Volumes Logs Stats (CPU / RAM usage) All in one place. ⚡ Why it matters (real productivity boost): No need to memorize long Docker commands Instant log viewing (no more copy-paste container IDs) One-click start/stop/restart containers Easy debugging inside a visual TUI Perfect for DevOps engineers and backend developers 🧠 Install in seconds: brew install lazydocker Then just run: lazydocker 💡 Final thought: Sometimes productivity isn’t about learning more tools — it’s about using smarter interfaces for the tools you already use. LazyDocker is one of those “why didn’t I use this earlier?” tools. #DevOps #Docker #LazyDocker #Containers #Linux #CloudComputing #DevOpsTools #BackendDevelopment #Terminal #Automation #ProductivityHacks #SoftwareEngineering

🔧 Real DevOps problems I solved this week — and what I learned. Setting up a full CI/CD pipeline from scratch sounds simple until you hit the real-world issues nobody talks about in tutorials. Here's what broke and how I fixed it 👇 ❌ Issue 1 — Jenkins wouldn't start Root cause: My Docker container (Tomcat) was already occupying port 8080. Jenkins also defaults to 8080. Simple conflict, but it took reading journalctl logs to find it. ❌ Issue 2 — App deployed but returned 404 Root cause: Tomcat maps WAR filenames directly to URL paths. maven-web-app.war serves at /maven-web-app — not /. Spent 20 minutes on this one. ❌ Issue 3 — Jenkins couldn't run Docker commands Root cause: Jenkins user wasn't in the docker group. One command fixed it: sudo usermod -aG docker jenkins ✅ End result: A working Jenkins pipeline that — → Clones code from GitHub → Builds WAR with Maven → Builds Docker image → Pushes to DockerHub The biggest lesson? Read the logs first. Always. #DevOps #Jenkins #Docker #AWS #CI_CD #CloudEngineering #Linux

𝗜 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝗺𝘆 𝗗𝗼𝗰𝗸𝗲𝗿 𝗖𝗼𝗺𝗺𝗮𝗻𝗱 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗠𝗮𝗻𝘂𝗮𝗹 A cheat sheet built for engineers who are tired of Googling the same flags over and over. Here's what's inside: 📦 Part 1 — Container Lifecycle Every command you need to run, create, exec into, stop, kill, restart, pause, list, remove, rename, update, and copy containers — with flags organized by skill level (Beginner → Intermediate → Advanced). 📊 Part 2 — Logs & Monitoring Complete flag breakdowns for docker logs, docker stats (with a full column reference), docker inspect with real Go template examples, plus docker top, docker events, and docker system df. ⚡ Quick Reference Tables At-a-glance command tables so you never have to dig through docs mid-deployment. 🧠 10 Key Takeaways The core principles every engineer should internalize to actually understand what Docker is doing under the hood — not just copy-paste commands. Whether you're just getting started with containers or you're managing production workloads daily, this reference will save you time. #Docker #DevOps #SoftwareEngineering #Containers #Linux #CloudNative #Programming #CheatSheet

Episode 10 of my journey to becoming a DevOps Engineer 🚀 In this episode, I’m diving into Docker and containerization. Before containerization, we relied heavily on virtual machines (VMs) to run multiple applications or services on a single server or PC. However, each VM requires its own operating system, which makes them heavy, slower to boot, and resource-intensive. To solve these challenges, containerization emerged. 1. In 2006, Cgroups were introduced 2. In 2008, LXC (Linux Containers) came along 3. In 2013, Docker was released — and it quickly became the most popular containerization platform Containers are lightweight because they share the host OS kernel. This means: 1. Faster startup times ⚡ 2. Better resource efficiency 💻 3. Reduced costs (time, infrastructure, and maintenance) 💰 🔧 Docker Runtime The runtime responsible for creating and managing containers is called containerd. The core server-side engine of Docker is known as dockerd (Docker daemon). 📦 Key Docker Components 1. Dockerfile – A script used to build Docker images 2. Image – A blueprint or snapshot of a container 3. Container – A running instance of an image 4. Volume – Persistent storage for containers 5. Network – Enables communication between containers Command for installing docker: sudo apt update sudo apt install docker.io sudo usermod -aG docker $USER sudo reboot For downloading an image: docker pull <image_name>:latest For running a container: docker run <image_name>:latest To execute something inside a running container docker exec -it <container_id> <what you want to execute> #AWS #Python #DevOps #Debugging #Learning #Programming #PDB #VSCode #CloudEngineering #CICD #Linux #GitHub #Git #bongoDev #Networking #InfrastructureAsCode #DevOpsJourney #CloudComputing #LearningInPublic

🚀 Debugging a small issue that taught me a bigger DevOps lesson While working on my Flask-based Status API and setting up a CI pipeline with GitHub Actions, I ran into an issue that looked simple on the surface but made me think deeper about environments. I was trying to create the .github/workflows directory using a command I had seen commonly used: mkdir -p .github/workflows It kept failing with a syntax error. At first, I thought I had made a typo, but the real issue was more subtle — I was running this on Windows CMD, where that syntax doesn’t work the same way as in Linux. After digging into it, I fixed it by explicitly creating the directory structure: mkdir .github mkdir .github\workflows This small fix made me realize something important: DevOps is not just about tools like Docker or CI/CD pipelines, but about understanding how different environments behave and how small assumptions can break automation. 🔹 What this project covers: - Flask-based API with structured JSON responses - Dockerfile for containerizing the application - GitHub Actions pipeline triggered on every push - Automated dependency installation and Docker image build - Basic monitoring exposure using a /metrics endpoint 🔹 Key takeaway: Even a minor issue like directory creation can break an entire CI pipeline if the structure is not exactly what the system expects (.github/workflows in this case). Attention to detail really matters in automation. Still building, still learning — focusing on getting the fundamentals right. 🔗 Project Repo: https://lnkd.in/gCfZZM4M #DevOps #CI #Docker #GitHubActions #Python #Flask #LearningInPublic

Most developers know how to write a Dockerfile. Fewer know how to wire it into a pipeline that actually ships software. In my latest blog post, I document the exact GitLab CI/CD setup I used to automate the full deployment of a Vite React app on Docker — from running tests in an isolated Node.js container, to building and pushing a Docker image, to deploying to a live Ubuntu server via SSH. A few things worth highlighting: → Docker-in-Docker (DinD) isn't magic — it's just the Docker daemon running as a service container inside your job → Never put credentials in your YAML. GitLab's CI/CD variable system exists for a reason. → Stage ordering is architecture. Test → Build → Deploy isn't arbitrary — it's a failure-isolation strategy. If your team is still deploying manually, this post is a starting point worth bookmarking. 🔗 https://lnkd.in/exJVzMN2 #DevOps #GitLab #Docker #CI_CD #EnterpriseArchitecture #SoftwareEngineering

🚀 End-to-End Deployment Automation with Bash & Docker I recently worked on a hands-on DevOps challenge where I automated the deployment of a Django Notes application using a Bash script — and it was a great learning experience! 🎯 Objective: Build a complete deployment pipeline using simple Bash scripting. 🛠️ What I implemented: 🔹 Cloned the application repository 🔹 Installed all required dependencies 🔹 Built a Docker image for the application 🔹 Pushed the image to Docker Hub 🔹 Deployed the container on a VM 🔹 Verified the deployment by checking container status 💡 Key Takeaways: ✔️ Learned how to structure Bash scripts for real-world automation ✔️ Improved understanding of Docker workflows (build, tag, push, run) ✔️ Gained practical exposure to deployment on a virtual machine ✔️ Understood how small scripts can simplify repetitive DevOps tasks Thanks Shubham Londhe for the repo "django-notes-app". I am able to deploy this application on my ubuntu-server VM. It was a good learning experience. 😊 #DevOps #BashScripting #Docker #Automation #CloudComputing #Linux

Day 39 of #90DaysOfDevOps — Today I didn't write a single pipeline. Instead, I spent the day understanding WHY CI/CD exists before touching any tooling. Here's what clicked for me today: 🔴 The Problem Imagine 5 developers all manually deploying to production. Merge conflicts, config mismatches, "it works on my machine" — a team can safely deploy maybe 1-2 times a day before mistakes creep in. CI/CD teams deploy hundreds of times a day. 🟡 CI vs CD vs CD • Continuous Integration — push code frequently, automatically build and test it, catch breaks in minutes not days • Continuous Delivery — pipeline is automated, but a human approves the final production release • Continuous Deployment — zero human involvement, code goes live automatically if all tests pass The difference between Delivery and Deployment? One human approval gate. 🟢 Real World I opened FastAPI's GitHub repo and read their test.yml workflow. Every pull request automatically runs tests across Windows, macOS and Ubuntu on Python 3.10 through 3.14. If any test fails, the PR cannot merge. That's not a pipeline failing. That's CI/CD doing exactly its job. Biggest lesson today: CI/CD is a practice, not a tool. GitHub Actions, Jenkins, GitLab CI — these are just tools that implement the practice. Day 40 tomorrow — time to actually build a pipeline. #90DaysOfDevOps #DevOpsKaJosh #TrainWithShubham #CICD #DevOps #CloudComputing

GitHub Branch Protection: Advanced Rules for Status Check Dependencies Master advanced branch protection configurations that go beyond basic reviews. Learn to set up dependent status checks, automatic review dismissal, and linear history enforcement for enterprise-grade code quality control. Read the full how-to guide: https://lnkd.in/gB3fSePc #ITTips #Productivity #DevOps #GitHub #TechTips #OpenSource #SoftwareDevelopment #BranchProtection #CodeQuality #GitWorkflow

Going from git push to a live production container - all on my own hardware. 🛠️ I recently shared a project I built for media processing. While the app itself was fun, deploying updates to my self-hosted environment was painful: manually build and push a new docker file, SSH into the server, pull the new image, spin up the new container. I wanted to streamline this process with professional-grade CI/CD pipeline, this was the real playground. The Pipeline: 1.  Version Control: Code pushed to GitHub. 2.  CI: GitHub Actions triggers an Ubuntu VM to build from my Dockerfile. 3.  Registry: The fresh image is pushed to my public Docker Hub repository. 4.  The "Handshake": GitHub Actions hits a custom Webhook Server I wrote running on my home lab. 5.  CD: My server pulls the latest image and restarts the container automatically. Was a full CI/CD pipeline necessary for a tool used by exactly two people? Probably not. But building it taught me more about the "plumbing" of DevOps - like handling GitHub actions, securing all my secrets!! webhooks and Docker registries - core concepts i can apply to future development, all while having fun building something simple. And most importantly, removing friction from my own workflow with one-click deployment. The biggest hurdle? It wasn't the Webhook server or the Docker orchestration. It was spending longer than i care to admit figuring out why my test downloads were "corrupted" on my Linux machine, only to realize I just needed to install VLC to support the MP4/MKV codecs. Sometimes the "bug" is just your media player! 😂 It’s not just about the final tool; it’s about having a playground to break things and fix them. #DevOps #Docker #GithubActions #SelfHosted #HomeLab #BackendEngineering