Bitbucket vs GitHub in 2026 isn’t a popularity contest. It’s about what fits your stack and your team’s habits. This guide breaks down the real differences in workflows, automation, integrations, and cost so you can choose confidently. Link in the comments 👇 #Bitbucket #GitHub #CICD #DevOps

I was working on a GitHub Project with custom fields like Week, Month, and Date to track when issues are closed. We standardized the project template across the organization, so all projects use the same custom fields. Which also means anyone working on these projects needs to keep them updated. At first, we did it manually, and that was fine. But across multiple projects, it became time-consuming and easy to forget. That’s when automation made sense, not because it’s always the answer, but because here it actually reduced effort. So I built a custom GitHub Action. Add a simple config JSON file, and once the issue is closed, the fields are updated automatically. #GitHub #Automation #DevOps #Productivity #ReusabilityMindset

Day 6 of 15 – Git & GitHub: Real Production Mistakes. Pushed a wrong commit to main? 😰 Happens in real projects more than you think. Real-world mistake 👇 - Debug code pushed - Wrong config committed - Feature not ready but merged DevOps rule ✅ ❌ Never rewrite history on shared branches ✅ Use git revert Read full content here: https://lnkd.in/gXdNy3QZ #Git #GitHub #DevOps #Troubleshooting #LearningInPublic

GitHub Actions – CI/CD Natively Integrated with GitHub GitHub Actions enables teams to build, test, and deploy applications directly from GitHub repositories. Why teams choose GitHub Actions: ✅ YAML-based workflows stored alongside application code ✅ Native integration with GitHub repos, PRs, and issues ✅ Secure secrets management and environment isolation ✅ Marketplace of reusable actions for faster pipeline creation GitHub Actions simplifies CI/CD by reducing tooling overhead while maintaining speed, security, and traceability. #GitHubActions #DevOps #CICD #CloudAutomation #GCP #CloudEngineering

How configuration and secrets really flow in Kubernetes 🔐 Many people use Kubernetes but are confused about how YAML, ConfigMap, Vault and Pods are connected. Here is the real picture: Git → YAML → Kubernetes → Pod YAML All configuration is written as YAML and stored in Git (GitLab/GitHub). This tells Kubernetes what to create. Kubernetes Kubernetes reads the YAML and creates Deployments, Pods, Services and ConfigMaps. ConfigMap ConfigMap stores non-secret configuration like: API URLs Log levels Feature flags These are injected into Pods as environment variables or files. Vault Vault stores secrets like: Database passwords API keys Tokens Pods securely pull secrets from Vault at runtime. Pod The Pod runs the application and receives: Config from ConfigMap Secrets from Vault The app never sees YAML — it only sees the final values. This separation is what makes Kubernetes secure, flexible and production-ready 🚀 #Kubernetes #DevOps #ConfigMap #Vault #CloudComputing #Microservices

Devops 101 #2 Podman Compose: An alternative to Docker Compose - Podman compose is a more secure alternative to Docker compose for container management, as it eliminates the need for a privileged daemon and supports rootless containers. - It is compatible with the same Compose YAML files and offers native systemd integration. - However, it has a smaller feature set and ecosystem compared to Docker Compose. - Hence, it is recommended to test it in a staging environment before migrating. #container #podman #docker

This is the exact CI/CD flow used in real production systems — not tutorials. Code doesn’t go to prod by luck. It passes through quality gates, containers, orchestration, and controlled rollouts. What this pipeline shows: • PR → automated CI checks • Docker image build • Code quality validation • Kubernetes deployment • Canary release (10% → 90%) • Real-time monitoring If CI/CD still feels confusing, save this. You’ll see this architecture in every serious tech company. #CICD #DevOps #Kubernetes #Docker #github

🚀 Deep Dive into GitLab CI/CD – Learning by Doing Over the past few days, I’ve been working hands-on with GitLab CI/CD, and it has significantly strengthened my understanding of how modern DevOps pipelines actually work in real-world scenarios. Here’s what I explored and implemented 👇 🔹 GitLab Runner setup on EC2 (Linux) 🔹 Understanding concurrent vs request_concurrency and why both matter 🔹 Fixing long-polling warnings and optimizing runner performance 🔹 Shell executor fundamentals and job execution flow 🔹 How GitLab picks jobs, queues them, and executes pipelines 🔹 Debugging common runner configuration issues (config.toml) What stood out the most is how GitLab CI/CD brings clarity and control to the entire software delivery lifecycle — from code commit to automated build, test, and deployment. This journey reinforced one key lesson for me: 👉 DevOps is not about tools alone — it’s about understanding behavior, performance, and reliability. 📌 I’ve also created a clean and practical .gitlab-ci.yml pipeline covering real use cases. 💬 Comment “CI” below if you want the GitLab CI/CD YAML file, and I’ll share it. Always learning. Always building. #GitLab #GitLabCI #DevOps #CICD #AWS #Linux #CloudComputing #Automation #ContinuousIntegration #LearningByDoing

❌ Using git add and git push without knowing what happens in between? That’s where most Git mistakes happen. 🚀 Day 7/100 – Git Architecture in Simple Words 🔁 Git Workflow Working Tree → Staging Area → Local Repo → GitHub 📝 Working Tree You write code in your project folder. 📦 Staging Area ---> git add file Tells Git what to track. 💾 Local Repository ---> git commit -m "message" Saves changes locally. ☁️ Remote Repository (GitHub) ---> git push Shares code with your team. 💡 Why this matters? This exact flow powers CI/CD pipelines and DevOps automation. 📌 Save this post — you’ll need it daily. #DevOps #Git #GitHub #VersionControl #SCM #CloudComputing #DevOpsEngineer #AWS #Azure #CICD #100DaysOfCloud #LearningInPublic #DevOpsCommunity #100DaysOfSRTechOpsDevOpsCloudChallenge #Linux #CIpipeline #GitWorkflow #DevOpsTools

⚙️ Before using Terraform with GitLab CI/CD, infrastructure changes felt risky. Terraform was sometimes run locally. State files lived on machines. Changes weren’t always tracked clearly. That approach didn’t scale — especially as projects evolved with frequent updates. Moving Terraform into GitLab pipelines changed the workflow completely. Now: * Infrastructure code lives in Git * Every change goes through a Merge Request * terraform plan runs automatically in CI * terraform apply is controlled and approved As the project grew, another thing became critical: tagging and versioning. We started using consistent tags across resources: * environment (dev / stage / prod) * application or project name * owner or team * cost and billing context These tags made it much easier to: * track changes across multiple versions * understand why a resource exists * manage cost and ownership Combined with Git history, this gave us: * a clear audit trail * visibility into who changed what and why * confidence to handle numerous infrastructure updates 🧠 The biggest mindset shift: 'Infrastructure changes should be versioned, reviewed, and traceable — just like application releases.' ->GitLab doesn’t just run Terraform. ->It helps control change in fast-moving environments. ->Still refining pipelines, tags, and workflows — but this approach has already reduced surprises. #Terraform #GitLab #DevOps #InfrastructureAsCode #AWS #CI_CD #CloudEngineering #CloudGovernance