GitHub Push Triggers DevOps Pipeline

🗓️ Day 29/100 — 100 Days of AWS & DevOps Challenge Today's task wasn't just Git — it was the full engineering team workflow that makes collaborative development actually safe. The requirement: Don't let anyone push directly to master. All changes must go through a Pull Request, get reviewed, and be approved before merging. This is branch protection in practice. Here's the full cycle: Step 1 — Developer pushes to a feature branch (already done) $ git log --format="%h | %an | %s" # Confirms user commit, author info, commit message Step 2 — Create the PR (Log into GIT) - Source: story/fox-and-grapes - Target: master - Title: Added fox-and-grapes story - Assign a user as reviewer Step 3 — Review and merge (log into GIT as reviewer) - Files Changed tab — read the actual diff - Approve the PR - Merge into master Master now has user story. And there's a full audit trail of who proposed it, who reviewed it, who approved it, and when it merged. Why this matters beyond the task: - A Pull Request is not a Git feature - it's a platform feature. Git only knows commits and branches. The PR is a Git/GitHub/GitLab construct that adds review, discussion, approval tracking, and CI/CD status checks on top of a branch merge. When companies say "we require code review before anything goes to production," this is the mechanism. When GitHub Actions or GitLab CI runs tests on every PR — this is where that hooks in. When a security audit asks "who approved this change?" — the PR has the answer. The workflow is identical across Git, GitHub, GitLab, and Bitbucket: push branch → open PR → assign reviewer → review diff→ approve → merge → master updated → branch deleted Full PR workflow breakdown on GitHub 👇 https://lnkd.in/gpi8_kAF #DevOps #Git #PullRequest #CodeReview #Gitea #BranchProtection #100DaysOfDevOps #KodeKloud #LearningInPublic #CloudEngineering #GitOps #TeamCollaboration

🚀 GitHub for DevOps – Day 4 (Part 2) 🔥 Git Reset Practical — Hands-on Lab Let’s stop theory and actually see what happens 👇 🧪 Step 1: Create Repo mkdir git-reset-lab cd git-reset-lab git init 🧪 Step 2: Create Commits echo "Version 1" > file.txt git add . git commit -m "v1 commit" echo "Version 2" >> file.txt git add . git commit -m "v2 commit" echo "Version 3" >> file.txt git add . git commit -m "v3 commit" 👉 Current state: v1 → v2 → v3 🔥 PART 1: SOFT RESET git reset --soft HEAD~1 ✔ v3 commit removed ✔ Changes still staged 👉 Check: git status 💡 Perfect for fixing commit messages 🔥 PART 2: MIXED RESET git reset HEAD~1 ✔ Commit removed ✔ Changes remain in file ❌ Not staged 👉 You must run: git add . 🔥 PART 3: HARD RESET (⚠️ Dangerous) git reset --hard HEAD~1 ❌ Commit removed ❌ Changes deleted permanently 👉 File goes back to: Version 1 Version 2 🧠 Final Understanding Soft → Keep changes staged Mixed → Keep changes in files Hard → Delete everything 💬 If you cannot control Git history, you cannot survive in real DevOps environments. Follow for more real-world DevOps learning 🚀 #HiteshDevOps #DevOps #Git #GitHub #CI_CD #Docker #Kubernetes #AWS #OpenToWork #HiringDevOps #DevOpsEngineer #TechHiring   #LearningInPublic #BuildInPublic #TechJourney

Stop just "saving" code. Start mastering it. Whether you're a developer, a DevOps engineer, or a tech enthusiast, understanding Git and GitHub isn't just a skill—it’s the "Single Source of Truth" for modern software delivery. I’ve been diving deep into Version Control Systems (VCS) recently, and I wanted to break down the core concepts that every tech professional should have in their toolkit. Centralized vs. Distributed (CVCS vs. DVCS) Old school (SVN/Perforce) relied on one central server. If it went down, work stopped. 🛑 Modern school (Git/Mercurial) is distributed. Every developer has the full history. If the server dies, the code lives on your machine. 🛡️ Git vs. GitHub: What's the difference? Git: The engine under the hood. It’s the local software that tracks your changes. GitHub: The social club for code. It’s the cloud platform where we collaborate, review PRs, and run CI/CD pipelines. The 3 States of Git (Your Workflow Path) Understanding how code moves is key to avoiding merge nightmares: 🔹 Working Directory: Where you write and modify files. 🔹 Staging Area: The "prep zone" where you pick what changes to include. 🔹 Git Directory: The final snapshot where history is permanently recorded. Pro-Tips for the DevOps Workflow: Branching: Work in isolation (feature-login, hotfix-patch) to keep production safe. 🌿 .gitignore: Always hide your secrets! Keep node_modules and .env files out of your repo. 🔒 Forking: The ultimate way to contribute to Open Source. Copy, modify, and propose changes via Pull Request. Version control is the foundation of automation, quality, and high-velocity delivery. If you aren't using Git, you aren't doing DevOps! What’s one Git command you can’t live without? Let’s chat in the comments! 👇 #DevOps #Git #GitHub #VersionControl #SoftwareEngineering #TechLearning #CloudComputing #OpenSource #SoftwareDevelopment #CareerGrowth#DevOps

👨💻 50-day journey to revisit and strengthen my DevOps engineering skills 📌 Day 2/50 📌 ⚙️ Tools I’ll be working with: Git | GitHub Actions Today, I focused on how local Git operations turn into real CI/CD execution using GitHub Actions. In production environments, every pipeline execution is a result of developer actions performed locally, making it critical to understand the complete flow from code creation to deployment trigger. 🔄 Flow Overview: 💠 Developer writes code locally 💠 Commits and pushes using Git 💠 Code is pushed to GitHub 💠 GitHub Actions workflow is triggered 💠 CI/CD pipeline executes (build, test, deploy) ➡️ Flow attached below 👇 📌 Note: Key CI/CD stability considerations as covered in Day 1 are critical. ➡️ For a more detailed understanding of GitHub Workflows and Actions, I’ve referred to the official documentation—feel free to explore it for deeper insights  🔗 https://lnkd.in/gm77PY7y 🚨 Possible Issue Scenario: A CI pipeline was set to trigger on every push to the main branch. A developer pushed changes without syncing with the latest remote updates, causing merge conflicts and pipeline failure due to inconsistent code. 🛠️ Resolution: Pulled latest changes from the repository Resolved conflicts locally Pushed updated code Pipeline re-triggered and executed successfully 💡 Always sync before pushing. ➡️ The key takeaway is that CI/CD pipelines are only as reliable as the version control practices behind them. Proper use of git pull, disciplined commits, and clean synchronization between local and remote repositories ensures smooth pipeline execution and avoids unnecessary failures. #DevOps #git #cicd #Github #skills #GithubCommands #Branchingstrategy #GithubActions #GithubDocs #Reskill #Workflow #Syntax

If you are working with Git in DevOps, you need to understand Git LFS. Here is why 👇 There are use cases where you might need to push a large file to git. For example, - ML models - Datasets etc. But Git was designed for text files, not binaries. Every change stores the entire file again, which bloats your repository fast. GitHub also has a hard limit of 100MB per file. Also, large files make git clone slow and git pull heavy. This directly impacts your CI pipeline performance. This is exactly where Git Large File Storage (LFS) helps you. It is an open source Git extension for versioning large files. Instead of storing large files directly in your repo, Git LFS replaces them with a small pointer file. That pointer file contains the LFS version, a SHA-256 hash of the actual file, and the file size. The real file is stored separately in Git LFS storage, not inside your Git repository. So where does it actually store the file? If you are using GitHub, the large file is stored in GitHub’s managed storage, separate from your Git objects. If you're self-hosting, it can be stored in your own LFS server or object storage like S3. Tools like DVC use similar workflow for versioning data in MLOPS use cases. --- 19000+ engineers read our DevOps/MLOPS newsletter. 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 (𝗶𝘁’𝘀 𝗳𝗿𝗲𝗲): https://lnkd.in/guFma5_V #mlops

🚀 Just finished the Docker course on Boot.dev! 🚀 I’m excited to share that I’ve learned the fundamentals of Docker—a key technology in modern DevOps and CI/CD pipelines. Docker makes it simple and fast to deploy new versions of code by packaging applications and their dependencies into preconfigured environments. This not only speeds up deployment, but also reduces overhead and eliminates the “it works on my machine” problem. Docker is a core part of the CI/CD (Continuous Integration/Continuous Deployment) process, enabling teams to deliver software quickly and reliably. Here’s a high-level overview of a typical CI/CD deployment process: The Deployment Process: 1. The developer (you) writes some new code 2. The developer commits the code to Git 3. The developer pushes a new branch to GitHub 4. The developer opens a pull request to the main branch 5. A teammate reviews the PR and approves it (if it looks good) 6. The developer merges the pull request 7. Upon merging, an automated script, perhaps a GitHub action, is started 8. The script builds the code (if it's a compiled language) 9. The script builds a new docker image with the latest program 10. The script pushes the new image to Docker Hub 11. The server that runs the containers, perhaps a Kubernetes cluster, is told there is a new version 12. The k8s cluster pulls down the latest image 13. The k8s cluster shuts down old containers as it spins up new containers of the latest image This process ensures that new features and fixes can be delivered to users quickly, safely, and consistently. image credit: Boot.dev Docker course #docker #cicd #devops #softwaredevelopment #bootdev #learning

🚀 DevOps Journey – Day 20 / 100 Jenkins - 1 👉 Still deploying code manually? You’re wasting time 😳 Today I started Jenkins & CI/CD Pipeline 🔥 --- 🔹 🧠 What is Jenkins? Jenkins is an automation tool used to build, test, and deploy code automatically. It works using plugins and supports almost every DevOps tool. --- 🔹 🔄 What is CI/CD? ✅ CI (Continuous Integration) Developers push code → automatically build & test ✅ CD (Continuous Delivery) Code is ready anytime → need approval to deploy ✅ CD (Continuous Deployment) Push code → directly goes live 🚀 --- 🔹 🔥 Real-Time Flow Developer writes code → Push to GitHub → Jenkins triggers build → Testing → Build → Deploy to server 👉 This is called CI/CD Pipeline (automated SDLC) --- 🔹 🛠️ Tools Jenkins | GitHub Actions | GitLab CI/CD | Azure Pipelines | AWS CodePipeline --- 🔹 ⚙️ Practical (Jenkins Setup in AWS EC2) Launch EC2 → Connect → become root Install Java: yum install java-17-amazon-corretto -y Install Jenkins: wget -O /etc/yum.repos.d/jenkins.repo https://lnkd.in/gtmtawJF rpm --import https://lnkd.in/g7gAZthp yum install jenkins -y Start Jenkins: systemctl start jenkins systemctl enable jenkins Open browser: http://<Public-IP>:8080 👉 If not opening → allow port 8080 in security group Unlock Jenkins: cat /var/lib/jenkins/secrets/initialAdminPassword Copy → paste in browser Install plugins → create user → Start Jenkins --- 💡 Pro Tip: Always automate deployment to save time & avoid human errors 🚀 Next: Creating Jenkins Job #DevOps #Jenkins #CICD #AWS #Automation #100DaysOfDevOps #MultiCloud #selflearning #frontlinemedia Frontlines EduTech (FLM)

📘 #100DaysOfDevOps – Day 12 (GitHub – Day 3) Today I learned about Merge Conflicts in Git and how to resolve them ⚔️ Merge conflicts happen when multiple changes are made to the same file/line in different branches, and Git is unable to decide which change to keep. --- 🔹 When do Merge Conflicts occur? • Same file edited in multiple branches • Same line modified differently • While merging branches --- 🔹 Example of Conflict <<<<<<< HEAD Hello from main branch ======= Hello from feature branch >>>>>>> feature-1 --- 🔹 How to Resolve Conflict 1️⃣ Open the conflicted file 2️⃣ Choose correct changes (or combine both) 3️⃣ Remove conflict markers ("<<<<<<", "======", ">>>>>>") 4️⃣ Add and commit changes git add file.txt git commit -m "Resolved merge conflict" --- 🔹 Useful Commands Check status "git status" Abort merge "git merge --abort" --- 🔹 Best Practices • Pull latest changes before working • Work on separate branches • Keep commits small and clear --- 💡 Key Learning: Handling merge conflicts is an essential skill for collaboration and maintaining clean code in real-world DevOps projects. Learning something new every day 🚀 #100DaysOfDevOps #DevOps #Git #GitHub #LearningInPublic

𝗗𝗮𝘆 𝟭𝟵 – 𝗗𝗼𝗰𝗸𝗲𝗿 | 𝟯𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗗𝗲𝘃𝗢𝗽𝘀 🐳 Docker is a powerful containerization platform that allows developers to package applications and their dependencies into lightweight, portable containers. Let’s understand how Docker works in DevOps 👇 🔹 𝗪𝗵𝘆 𝗗𝗼𝗰𝗸𝗲𝗿 𝗜𝘀 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 ✔ Ensures consistency across environments (Dev → Test → Prod) ✔ Lightweight compared to virtual machines ✔ Faster deployment and scaling ✔ Simplifies dependency management 🔹 𝗞𝗲𝘆 𝗗𝗼𝗰𝗸𝗲𝗿 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 • Image → Blueprint of an application • Container → Running instance of an image • Dockerfile → Script to build Docker images • Registry → Storage for Docker images (Docker Hub, ECR) • Volume → Persistent storage for containers 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝗻 𝗗𝗲𝘃𝗢𝗽𝘀 1️⃣ Developer writes code 2️⃣ Create Dockerfile 3️⃣ Build Docker image 4️⃣ Run container locally 5️⃣ Push image to registry 6️⃣ Deploy container to server/Kubernetes 🔹 𝗕𝗮𝘀𝗶𝗰 𝗗𝗼𝗰𝗸𝗲𝗿 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀 • docker build -t myapp . → Build image • docker run -d -p 80:80 myapp → Run container • docker ps → List running containers • docker images → List images • docker stop <container_id> → Stop container • docker rm <container_id> → Remove container 🔹 𝗦𝗮𝗺𝗽𝗹𝗲 𝗗𝗼𝗰𝗸𝗲𝗿𝗳𝗶𝗹𝗲 FROM openjdk:8 COPY target/app.jar app.jar ENTRYPOINT ["java","-jar","app.jar"] 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 𝘃𝘀 𝗩𝗶𝗿𝘁𝘂𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 • Docker → Lightweight, shares OS kernel • VM → Heavy, runs full OS • Docker → Fast startup • VM → Slower boot time 🔹 𝗗𝗼𝗰𝗸𝗲𝗿 𝗶𝗻 𝗖𝗜/𝗖𝗗 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 Git → Jenkins → Build (Maven) → Docker Build → Push Image → Deploy 💡 𝗦𝗶𝗺𝗽𝗹𝗲 𝗙𝗹𝗼𝘄 Code → Dockerfile → Image → Container → Deployment 🔥 Docker is a must-have tool for every DevOps Engineer to build scalable and portable applications. Follow my 30 Days of DevOps series for more learning 🚀 #DevOps #Docker #Containers #CI_CD #Jenkins #Kubernetes #AWS #CloudComputing

Most CI/CD pipelines fail for the same reason — no clear stages. After 4 years in DevOps, here's the multi-stage GitHub Actions pipeline I recommend to every engineer on my team: ━━━━━━━━━━━━━━━━━━━ Stage 1 → Test Stage 2 → Build & tag Docker image Stage 3 → Deploy to Staging Stage 4 → Deploy to Production (with manual approval) ━━━━━━━━━━━━━━━━━━━ 3 things that make this bulletproof: 1️⃣ Use needs: to chain jobs — if tests fail, nothing else runs 2️⃣ Tag images with github.sha — every build is fully traceable 3️⃣ Use GitHub Environments for prod — enforces human approval before anything goes live You don't need a complex tool to do this. A single YAML file in .github/workflows/ is enough to build a production-grade pipeline. Save this post for when you set yours up. What does your CI/CD stack look like? Drop it in the comments 👇 #DevOps #GitHubActions #CICD #Docker #Kubernetes #CloudNative #DevOpsEngineer #SoftwareEngineering