DEV Community: Bhaskar Sharma

⚓ Day 22 of My DevOps Journey: Strengthening Networking Basics 🌐

Bhaskar Sharma — Wed, 24 Sep 2025 03:40:55 +0000

Hello DevOps community! 👋

For the past 3 weeks, I’ve been laying the foundation of my DevOps learning — Linux, Bash scripting, and Git/GitHub. Today, I focused deeper on Networking Basics, a skill every DevOps engineer must master.

🔹 What I explored today

How DNS resolution works (from domain → IP)

Load Balancing concepts — distributing traffic across servers

Difference between TCP vs UDP in real-world applications

Basics of firewalls & ports (why security starts here)

🔹 Why this matters in DevOps
Networking is the backbone of cloud and infrastructure. Whether it’s deploying apps, configuring Kubernetes clusters, or setting up CI/CD pipelines, understanding how data flows across the network makes troubleshooting and scaling much easier.

🔹 My key takeaway
"DevOps isn’t just about tools — it’s about understanding the fundamentals that keep systems reliable and secure."

Excited for tomorrow as I continue building on these foundations before stepping into CI/CD (Day 31–60) soon 🚀

DevOps #Networking #Linux #DevOpsJourney #100DaysOfCloud #ContinuousLearning

⚓ Day 21 of My DevOps Journey: Kubernetes — Orchestrating Containers at Scale 🚀

Bhaskar Sharma — Tue, 23 Sep 2025 03:09:23 +0000

Hello DevOps community! 👋

For the past 20 days, I’ve been sharing my DevOps learning journey consistently.
To be honest — engagement has been slow on platforms like Dev.to & Medium. But that’s fine ✅ because these posts keep me accountable, help me learn, and document my growth. 🌱

Yesterday, I explored Docker — the magic that makes applications portable.
Today, I’m taking the next leap → Kubernetes (K8s), the platform that ensures containers run reliably at scale in production.

🔹 Why Kubernetes Matters

Imagine running hundreds of containers.
Who starts them? Who restarts them if they fail? Who balances traffic? 🤔

That’s exactly where Kubernetes comes in:

✅ Automates deployment, scaling & healing
✅ Distributes traffic with built-in load balancing
✅ Manages storage (Volumes & PVCs)
✅ Enables rolling updates with zero downtime

🧠 Core Concepts (Beginner-Friendly)

Pod → Smallest deployable unit (wraps one or more containers)

ReplicaSet → Ensures the right number of Pods are always running

Deployment → Manages ReplicaSets & rolling updates

Service → Exposes your app inside/outside the cluster

ConfigMap & Secret → Store configs & sensitive data securely

Persistent Volume (PV) → Storage that survives Pod restarts

🔧 Example: Simple Deployment YAML
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-deployment
spec:
replicas: 3
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: myapp:v1
ports:
- containerPort: 3000

👉 Deploy it with:

kubectl apply -f deployment.yaml

🛠️ DevOps Use Cases

Run microservices across clusters

Auto-scale workloads with HPA (Horizontal Pod Autoscaler)

Enable self-healing (Pods restart automatically)

Manage production workloads with confidence

⚡ Pro Tips for Beginners

Start with Minikube or Kind for local practice

Always use Namespaces to organize resources

Debug with kubectl describe & kubectl logs

Use YAML linting tools to avoid mistakes

🧪 Hands-on Mini-Lab (Try this!)

1️⃣ Install Minikube
2️⃣ Create the above Deployment
3️⃣ Expose it with a Service (kubectl expose)
4️⃣ Scale Pods up/down and watch the magic 🎩

🎯 Key Takeaway

Docker gave us containers.
Kubernetes gives us orchestration.

👉 If Docker is the car, Kubernetes is the traffic system 🚦 that keeps everything running smoothly at scale.

💬 Over to You

I’ve been posting daily for 21 days straight — but I’d love to hear from you 👇

👉 What was your first “aha moment” with Kubernetes?
👉 Do you prefer hands-on labs or deep-dive theory posts?

Your feedback will help me improve 🙏

🔖 #Kubernetes #Docker #DevOps #SRE #LearningInPublic

⚓ Day 21 of My DevOps Journey: Kubernetes — Orchestrating Containers at Scale 🚀

Bhaskar Sharma — Mon, 22 Sep 2025 02:17:55 +0000

Hello DevOps community! 👋

For the past 20 days, I’ve been consistently sharing my DevOps learning journey.
Truth be told — engagement has been slow on platforms like Dev.to & Medium. But that’s okay. Because every post keeps me accountable, helps me learn, and documents my progress. 🌱

Yesterday, I explored Docker, the magic that makes applications portable.
Today, I’m taking the next leap → Kubernetes (K8s), the platform that runs containers reliably at scale in production.

🔹 Why Kubernetes Matters

Imagine managing hundreds of containers.
Who starts them? Who restarts them if they fail? Who balances traffic? 🤔

That’s where Kubernetes steps in:

✅ Automates deployment, scaling & healing
✅ Distributes traffic with load balancing
✅ Manages storage (Volumes & PVCs)
✅ Enables rolling updates & zero downtime

🧠 Core Concepts (Beginner-Friendly)

Pod → Smallest deployable unit (wraps one or more containers)

ReplicaSet → Ensures your app always has the desired number of Pods

Deployment → Manages ReplicaSets & rolling updates

Service → Exposes your app inside/outside the cluster

ConfigMap & Secret → Store configs & sensitive data securely

Persistent Volume (PV) → Storage that survives Pod restarts

👉 Apply it:

kubectl apply -f deployment.yaml

🛠️ DevOps Use Cases

Run microservices across clusters

Auto-scale workloads with HPA (Horizontal Pod Autoscaler)

Self-healing apps (Pods restart automatically)

Manage complex production workloads with ease

⚡ Pro Tips for Beginners

Start with Minikube or Kind for local practice

Always use Namespaces to organize resources

Debug with kubectl describe & kubectl logs

Use YAML linting tools to avoid silly mistakes

🧪 Hands-on Mini-Lab (Try this!)

1️⃣ Install Minikube
2️⃣ Create the above Deployment
3️⃣ Expose it with a Service (kubectl expose)
4️⃣ Scale Pods up/down and watch the magic 🎩

🎯 Key Takeaway

Docker gave us containers. Kubernetes gives us orchestration.
If Docker is the car, Kubernetes is the traffic system 🚦 that ensures everything runs smoothly at scale.

💬 Over to You

I’ve been posting daily for 21 days — but I’d love to hear from you 👇

👉 What was your first “aha moment” when learning Kubernetes?
👉 Do you prefer hands-on labs or deep-dive theory posts?

Your feedback will help me improve 🙏

🔖 #Kubernetes #Docker #DevOps #SRE #LearningInPublic

⚓ Day 21 of My DevOps Journey: Kubernetes — Orchestrating Containers at Scale 🚀

Bhaskar Sharma — Sun, 21 Sep 2025 01:51:08 +0000

Hello DevOps community! 👋

For the past 20 days, I’ve been sharing my DevOps learning journey daily.
To be honest — consistency hasn’t translated into much engagement yet (on Dev.to or Medium). But I’m not stopping. Why? Because every post helps me learn, document, and stay accountable. 🌱

Yesterday, I explored Docker, the magic that makes applications portable.
Today, I’m taking the next big leap → Kubernetes (a.k.a. K8s), the platform that helps you run containers at scale in production.

🔹 Why Kubernetes Matters
Imagine you have 100 containers running. Who starts them? Who restarts them if they crash? Who makes sure traffic is distributed evenly? 🤔
That’s where Kubernetes steps in.

✅ Automates deployment, scaling & healing
✅ Distributes traffic with load balancing
✅ Manages storage (Volumes & PVCs)
✅ Enables rolling updates & zero-downtime deployments

🧠 Core Concepts in Kubernetes (Beginner-Friendly)

Pod → Smallest deployable unit (wraps one or more containers)

ReplicaSet → Ensures your app always has the desired number of Pods running

Deployment → Manages ReplicaSets & provides rolling updates

Service → Exposes your app inside/outside the cluster

ConfigMap & Secret → Store configs & sensitive data securely

Persistent Volume (PV) → Storage that survives Pod restarts

🔧 Example: Simple Deployment YAML

apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp-deployment
spec:
replicas: 3
selector:
matchLabels:
app: myapp
template:
metadata:
labels:
app: myapp
spec:
containers:
- name: myapp
image: myapp:v1
ports:
- containerPort: 3000

👉 Apply it:

kubectl apply -f deployment.yaml

🛠️ DevOps Use Cases

Run microservices across clusters

Auto-scale workloads with HPA (Horizontal Pod Autoscaler)

Self-healing apps (Pods auto-restart on failure)

Manage complex production environments with ease

⚡ Pro Tips for Beginners

Start with Minikube or Kind for local practice

Always use Namespaces to organize resources

Learn to debug with kubectl describe and kubectl logs

Use YAML linting to avoid silly mistakes

🧪 Hands-on Mini-Lab (Try this!)
1️⃣ Install Minikube
2️⃣ Create a Deployment (like above)
3️⃣ Expose it with a Service (kubectl expose)
4️⃣ Scale Pods up/down and see magic happen 🎩

🎯 Key Takeaway
Docker gave us containers. Kubernetes gives us production-grade orchestration.
If Docker is the car, Kubernetes is the traffic system 🚦 that ensures everything runs smoothly at scale.

💬 Over to You
I’ve been sharing daily for 21 days — but I’d love to hear from you.
👉 What was your first “aha moment” when learning Kubernetes?
👉 Do you prefer hands-on labs, or deep-dive theory posts?

Your feedback will help me make these posts better 🙏

🔖 #Kubernetes #Docker #DevOps #SRE #LearningInPublic

⚓ Day 20 of My DevOps Journey: Docker — Containerization Made Simple 🚀

Bhaskar Sharma — Sat, 20 Sep 2025 02:18:17 +0000

Hello dev.to community! 👋

Yesterday, I explored GitHub Actions, GitHub’s native CI/CD tool that makes automation seamless.
Today, I’m diving into Docker, the backbone of containerization in DevOps.

🔹 Why Docker Matters
Before Docker, apps often suffered from the classic “works on my machine” problem. Docker solves this by packaging applications with all dependencies into portable containers.

✅ Consistent environment across dev, test, and prod
✅ Lightweight and faster than virtual machines
✅ Easy scaling with orchestration tools (like Kubernetes)
✅ Huge ecosystem with Docker Hub images

🧠 Core Concepts in Docker

Image → Blueprint of your application (built using a Dockerfile)

Container → Running instance of an image

Dockerfile → Instructions to build an image

Registry → Storage for images (Docker Hub, ECR, GCR)

Volume → Persistent storage for containers

Network → Communication between containers

🔧 Example: Simple Dockerfile

Use Node.js base image

FROM node:18

Set working directory

WORKDIR /app

Copy package files and install dependencies

COPY package*.json ./
RUN npm install

Copy source code

COPY . .

Expose app port

EXPOSE 3000

Start the app

CMD ["npm", "start"]

👉 Build and run it:

docker build -t myapp .
docker run -p 3000:3000 myapp

🛠️ DevOps Use Cases

Package applications for CI/CD pipelines

Deploy microservices on Kubernetes or ECS

Create reproducible environments for testing

Run security scans (e.g., Trivy) on container images

⚡ Pro Tips

Keep images small → use slim/alpine base images

Use .dockerignore to avoid unnecessary files in builds

Tag images properly (e.g., myapp:v1.0.0)

Scan images for vulnerabilities before pushing to registry

🧪 Hands-on Mini-Lab (Try this!)
1️⃣ Install Docker Desktop
2️⃣ Write a Dockerfile for your app
3️⃣ Build and run the image locally
4️⃣ Push it to Docker Hub with docker push

🎯 Key Takeaway
Docker makes applications portable, scalable, and easy to manage — a cornerstone skill for every DevOps engineer.

🔜 Tomorrow (Day 21):
I’ll explore Kubernetes — the orchestration layer that takes Docker to production scale.

🔖 #Docker #DevOps #Containers #Automation #SRE

⚙️ Day 19 of My DevOps Journey: GitHub Actions — CI/CD Made Easy 🚀

Bhaskar Sharma — Fri, 19 Sep 2025 03:22:47 +0000

Hello dev.to community! 👋

Yesterday, I explored Jenkins, a powerful and widely used CI/CD tool for automating pipelines.
Today, I’m diving into GitHub Actions, GitHub’s native CI/CD solution that makes automation seamless for developers.

🔹 Why GitHub Actions Matters
Since most projects already live on GitHub, GitHub Actions integrates directly with your repos to provide:

✅ Native CI/CD without extra setup
✅ Event-driven workflows (push, pull request, issue, schedule, etc.)
✅ Reusable workflows & actions from GitHub Marketplace
✅ Easy integration with cloud providers (AWS, GCP, Azure)
✅ Free hosted runners with Linux, Windows, macOS

🧠 Core Concepts in GitHub Actions

Workflow → Automation definition (YAML file in .github/workflows/)

Job → A set of steps that run in a runner

Step → A single task (e.g., build, test, deploy)

Runner → The machine where jobs run (hosted or self-hosted)

Actions → Reusable tasks (community-built or custom)

🔧 Example: Simple Workflow

name: CI Pipeline

on: [push, pull_request]

jobs:
build-test-deploy:
runs-on: ubuntu-latest
steps:
- name: Checkout code
uses: actions/checkout@v4

  - name: Set up Node.js
    uses: actions/setup-node@v4
    with:
      node-version: '18'

  - name: Install dependencies
    run: npm install

  - name: Run tests
    run: npm test

  - name: Deploy
    run: echo "Deploying app..."

👉 Save this in .github/workflows/ci.yml and GitHub will trigger it automatically.

🛠️ DevOps Use Cases

Continuous Integration → Run tests on every commit

Continuous Delivery → Deploy to cloud or Kubernetes clusters

Infrastructure Automation → Trigger Terraform/Ansible workflows

Security & Compliance → Run scans with Trivy, Checkov, CodeQL

⚡ Pro Tips

Use secrets in GitHub for credentials (never hardcode them)

Reuse workflows across repos with workflow_call

Leverage GitHub Marketplace Actions instead of reinventing the wheel

Use matrix builds to test across multiple OS/versions

🧪 Hands-on Mini-Lab (Try this!)
1️⃣ Create a repo on GitHub
2️⃣ Add .github/workflows/ci.yml with the above workflow
3️⃣ Push code → watch the pipeline run in the Actions tab
4️⃣ Add a deploy step (Heroku, AWS, or DockerHub)

🎯 Key Takeaway
GitHub Actions brings CI/CD closer to your code, making automation simpler, faster, and developer-friendly. It’s a must-know tool for modern DevOps engineers.

🔜 Tomorrow (Day 20):
I’ll explore Docker — containerization to build, ship, and run applications anywhere.

🔖 #GitHubActions #DevOps #CI/CD #Automation #SRE

⚙️ Day 18 of My DevOps Journey: CI/CD with Jenkins — Automating Build, Test & Deploy Pipelines 🚀

Bhaskar Sharma — Thu, 18 Sep 2025 03:34:50 +0000

Hello dev.to community! 👋

Yesterday, I explored Ansible, a powerful configuration management tool that automates server setup and application deployment.
Today, I’m diving into Jenkins, one of the most popular CI/CD tools that automates software delivery pipelines.

🔹 Why Jenkins Matters
Modern software development requires frequent updates, testing, and deployments. Jenkins helps by:

✅ Automating builds, tests, and deployments
✅ Supporting 1,800+ plugins (integrates with almost everything)
✅ Scalable — can run on a single server or distributed agents
✅ Open-source & widely adopted across the industry

🧠 Core Jenkins Concepts

Pipeline → A series of steps (build → test → deploy)

Job/Project → A single automation task (e.g., build a Java app)

Agent/Node → Where Jenkins executes jobs

Plugins → Add integrations (Docker, Kubernetes, GitHub, etc.)

Jenkinsfile → Code representation of pipelines (declarative or scripted)

🔧 Example: Simple Declarative Pipeline

pipeline {
agent any
stages {
stage('Build') {
steps {
echo "Building the application..."
}
}
stage('Test') {
steps {
echo "Running tests..."
}
}
stage('Deploy') {
steps {
echo "Deploying application..."
}
}
}
}

👉 Save this in a Jenkinsfile and Jenkins will execute the pipeline automatically.

🛠️ DevOps Use Cases

Continuous Integration (CI) → Run unit tests on every commit

Continuous Delivery (CD) → Deploy apps to staging/prod automatically

Infrastructure pipelines → Integrate with Terraform & Ansible

Security scans → Integrate with tools like SonarQube, Trivy

⚡ Pro Tips

Use Jenkins pipelines as code (Jenkinsfile) for version control

Run Jenkins in Docker/Kubernetes for scalability

Secure Jenkins with role-based access control & credentials manager

Automate notifications (Slack, email) for pipeline status

🧪 Hands-on Mini-Lab (Try this!)
1️⃣ Install Jenkins (Docker is easiest 🚀)
2️⃣ Create a simple freestyle job to print “Hello DevOps”
3️⃣ Write a Jenkinsfile for a simple pipeline
4️⃣ Integrate GitHub → trigger builds on every commit

🎯 Key Takeaway
Jenkins automates the CI/CD pipeline, making software delivery faster, consistent, and reliable — a must-have skill for DevOps engineers.

🔜 Tomorrow (Day 19):
I’ll explore GitHub Actions — CI/CD natively built into GitHub.

🔖 #Jenkins #DevOps #CI/CD #Automation #SRE

⚙️ Day 17 of My DevOps Journey: Ansible — Automating Configuration Management 🚀

Bhaskar Sharma — Wed, 17 Sep 2025 01:37:16 +0000

Hello dev.to community! 👋

Yesterday, I explored Terraform, a powerful Infrastructure as Code (IaC) tool for provisioning cloud resources. Today, I’m diving into Ansible, a widely used tool for automating server configuration, application deployment, and orchestration.

🔹 Why Ansible Matters

While Terraform creates infrastructure, you still need to configure servers and applications running on them. That’s where Ansible shines:

✅ Agentless → Works over SSH (no agents to install)
✅ Idempotent → Ensures configs reach and stay in the desired state
✅ Scalable → Manage 10 or 1,000+ servers with the same playbook
✅ Declarative & Simple → YAML syntax makes it beginner-friendly

🧠 Core Ansible Concepts

Inventory → Defines your servers (hosts file with IPs/domains)

Modules → Reusable units to perform tasks (install package, copy file, etc.)

Playbook → YAML file describing tasks to apply to hosts

Roles → Structured way to organize playbooks for reusability

Ad-hoc Commands → Quick one-liners for immediate actions

🔧 Example: Simple Playbook

name: Install Nginx on Ubuntu hosts: webservers become: yes tasks:
- name: Install Nginx package apt: name: nginx state: present

👉 Run with:

ansible-playbook -i hosts install_nginx.yml

This will automatically install and configure Nginx on your defined servers.

🛠️ DevOps Use Cases

Automated server provisioning (installing software, setting users, configuring firewalls)

Application deployment (Java, Node.js, Python apps)

Managing secrets and configs

Works great with Terraform → Terraform provisions infra, Ansible configures it

⚡ Pro Tips

Use Ansible Galaxy for community roles (huge time-saver 🚀)

Store playbooks in Git for collaboration and version control

Integrate with Jenkins / GitHub Actions for automated deployments

Secure credentials using Ansible Vault

🧪 Hands-on Mini-Lab (Try this!)

1️⃣ Install Ansible on your machine
2️⃣ Create an inventory file (hosts) with your server IP
3️⃣ Write a playbook to install Nginx or Apache
4️⃣ Run ansible-playbook and verify the installation

🎯 Key Takeaway:
Ansible bridges the gap between infrastructure and applications, making configuration management and deployment fast, reliable, and repeatable — an essential skill for any DevOps engineer.

🔜 Tomorrow (Day 18):
I’ll explore CI/CD with Jenkins — automating build, test, and deploy pipelines. 🔄

🔖 #Ansible #DevOps #Automation #IaC #ConfigurationManagement #SRE

🚀 Day 16 of My DevOps Journey: Terraform — Automating Infrastructure as Code (IaC) 🌍

Bhaskar Sharma — Tue, 16 Sep 2025 02:25:04 +0000

Hello dev.to community! 👋

Yesterday, I explored GitLab CI — a powerful CI/CD tool that brings the full DevOps lifecycle into one platform. Today, I’m diving into Terraform, one of the most popular Infrastructure as Code (IaC) tools that helps you automate and manage cloud resources efficiently.

🔹 Why Terraform Matters

Instead of manually creating servers, databases, or networks, Terraform lets you define infrastructure in code. This makes infrastructure:

✅ Reproducible → Same config works across environments
✅ Scalable → Spin up or tear down infra quickly
✅ Version-controlled → Track changes in Git
✅ Cloud-agnostic → Works with AWS, Azure, GCP, Kubernetes, and more

🧠 Core Terraform Concepts

Provider → Plugin that lets Terraform interact with a cloud/service (AWS, Azure, etc.)

Resource → The actual infrastructure component (VM, DB, Load Balancer)

State File → Terraform’s record of what’s deployed (acts as the source of truth)

Plan & Apply → See proposed changes (terraform plan) and execute (terraform apply)

Modules → Reusable templates for standard infra setups

🔧 Example: Simple Terraform Configuration

provider "aws" {
region = "ap-south-1"
}

resource "aws_instance" "my_ec2" {
ami = "ami-12345678"
instance_type = "t2.micro"
}

👉 Run terraform init → terraform plan → terraform apply
Terraform will provision an EC2 instance automatically 🚀

🛠️ DevOps Use Cases

Provision cloud infra (VMs, networks, databases, storage)

Automate Kubernetes cluster setup

Manage multi-cloud environments with the same tool

Integrate with CI/CD (GitLab CI, GitHub Actions, Jenkins)

Use alongside Ansible for config management

⚡ Pro Tips

Always use remote state (S3 + DynamoDB for AWS) to collaborate safely

Use variables & outputs to make configs reusable

Organize infra with modules for production-grade setups

Run terraform destroy carefully — it deletes infra! ⚠️

🧪 Hands-on Mini-Lab (Try this!)

1️⃣ Install Terraform on your machine
2️⃣ Create a main.tf with an AWS provider + EC2 instance
3️⃣ Run terraform init → terraform apply
4️⃣ Verify the EC2 instance in your AWS Console

🎯 Key Takeaway:
Terraform is the backbone of IaC, making infrastructure consistent, automated, and scalable — a must-have skill for every DevOps engineer.

🔜 Tomorrow (Day 17):
I’ll explore Ansible — automating configuration management and server provisioning. ⚙️

🔖 #Terraform #DevOps #IaC #Automation #Cloud #SRE

🚀 Day 15 of My DevOps Journey: GitLab CI — Powerful Pipelines for Modern DevOps 🦊

Bhaskar Sharma — Mon, 15 Sep 2025 03:02:18 +0000

Hello dev.to community! 👋

Yesterday, I explored GitHub Actions — a cloud-native CI/CD tool integrated into GitHub. Today, I’m diving into GitLab CI, another popular CI/CD solution that’s both self-managed and cloud-native, offering end-to-end DevOps features in one platform.

🔹 Why GitLab CI Matters

Unlike tools that focus only on CI/CD, GitLab provides a complete DevOps lifecycle in a single application.

✅ Built-in CI/CD with no extra setup
✅ Powerful pipeline-as-code (.gitlab-ci.yml)
✅ Works for both self-hosted GitLab and GitLab.com
✅ Advanced features like auto DevOps, environments, and security scans

🧠 Core GitLab CI Concepts

Pipeline → Defines the overall CI/CD workflow

Jobs → Individual tasks (build, test, deploy)

Stages → Logical group of jobs (build → test → deploy)

Runners → Agents that execute jobs (shared or self-hosted)

Artifacts → Files created by jobs (logs, build outputs)

🔧 Example: Simple GitLab CI Pipeline

.gitlab-ci.yml

stages:

build
test
deploy

build_job:
stage: build
script:
- echo "Building application..."
- npm install

test_job:
stage: test
script:
- echo "Running tests..."
- npm test

deploy_job:
stage: deploy
script:
- echo "Deploying to production..."
only:
- main

👉 Save this as .gitlab-ci.yml in your repo root. GitLab will auto-detect and run the pipeline.

🛠️ DevOps Use Cases

Automate build → test → deploy workflows

Deploy applications to Kubernetes, AWS, GCP, or Azure

Run SAST/DAST security scans directly in pipelines

Manage infrastructure as code (Terraform + GitLab)

Set up review apps for each merge request

⚡ Pro Tips

Use GitLab Environments for staging/production tracking

Store sensitive data in GitLab CI/CD variables

Explore Auto DevOps for quick deployments

Combine with GitLab Container Registry for Docker images

🧪 Hands-on Mini-Lab (Try this!)

1️⃣ Create a repo in GitLab
2️⃣ Add a .gitlab-ci.yml file with build/test jobs
3️⃣ Push your code → GitLab CI pipeline runs automatically
4️⃣ Check job logs under CI/CD → Pipelines

🎯 Key Takeaway:
GitLab CI gives you flexibility, security, and scalability, making it a strong choice for teams that want an all-in-one DevOps platform.

🔜 Tomorrow (Day 16):
I’ll explore Automation with Terraform — the backbone of Infrastructure as Code (IaC). 🌍

🔖 #GitLabCI #DevOps #CICD #Automation #CloudNative #SRE

🚀 Day 14 of My DevOps Journey: GitHub Actions — CI/CD Made Easy in the Cloud ☁️

Bhaskar Sharma — Sun, 14 Sep 2025 02:49:47 +0000

Hello dev.to community! 👋

Yesterday, I explored Jenkins — a powerful on-premise CI/CD tool. Today, I’m stepping into GitHub Actions, a cloud-native CI/CD solution built directly into GitHub.

🔹 Why GitHub Actions Matters

Managing Jenkins servers can be heavy. GitHub Actions eliminates that by running pipelines directly in the cloud, triggered by repository events.

✅ Native to GitHub (no extra setup)
✅ Free tier with generous minutes for public repos
✅ Marketplace with 10,000+ pre-built actions
✅ Build, test & deploy directly from GitHub

🧠 Core GitHub Actions Concepts

Workflows → YAML files (.github/workflows/ci.yml) that define your pipeline.

Jobs → Group of steps executed on a runner.

Runners → Virtual machines (Linux/Windows/Mac) where jobs run.

Actions → Reusable steps (e.g., checkout code, setup Node.js, deploy to AWS).

🔧 Example: Simple GitHub Actions Workflow

.github/workflows/ci.yml

name: CI Pipeline

on:
push:
branches: [ "main" ]
pull_request:
branches: [ "main" ]

jobs:
build:
runs-on: ubuntu-latest

steps:
- name: Checkout code
  uses: actions/checkout@v3

- name: Set up Node.js
  uses: actions/setup-node@v3
  with:
    node-version: '18'

- name: Install dependencies
  run: npm install

- name: Run tests
  run: npm test

- name: Deploy
  run: echo "Deploying application..."

👉 Save this under .github/workflows/ in your repo. It will auto-trigger on push/pull requests to main.

🛠️ DevOps Use Cases

Automate build & test on every PR

Deploy apps to AWS, Azure, GCP, or Kubernetes

Run security scans with tools like Trivy or Snyk

Build and push Docker images automatically

Manage microservices CI/CD workflows

⚡ Pro Tips

Use matrix builds to test across multiple OS/language versions.

Store secrets in GitHub Secrets for secure pipelines.

Explore the GitHub Marketplace for ready-to-use actions.

Combine with GitHub Packages for container/image management.

🧪 Hands-on Mini-Lab (Try this!)

1️⃣ Create a repo (or use an existing one).
2️⃣ Add a workflow file under .github/workflows/ci.yml.
3️⃣ Push your code → GitHub Actions runs automatically.
4️⃣ Watch your build/test logs in the Actions tab.

🎯 Key Takeaway:
GitHub Actions brings serverless CI/CD directly into your GitHub workflow. It’s lightweight, scalable, and perfect for cloud-native DevOps.

🔜 Tomorrow (Day 15):
I’ll dive into GitLab CI — another powerful CI/CD tool that’s popular for self-managed and cloud-native DevOps pipelines. 🚀

🔖 #GitHubActions #DevOps #CICD #Automation #CloudNative #SRE

🚀 Day 13 of My DevOps Journey: Jenkins — Powering CI/CD Pipelines ⚙️

Bhaskar Sharma — Sat, 13 Sep 2025 02:18:19 +0000

Hello dev.to community! 👋

Yesterday, I explored Ansible — automating configuration management. Today, I’m diving into Jenkins, one of the most widely used tools for Continuous Integration & Continuous Delivery (CI/CD).

🔹 Why Jenkins Matters

Manually building, testing, and deploying applications slows down development. Jenkins automates this process, ensuring faster delivery with fewer errors.

✅ Open-source & widely adopted
✅ Plugin-rich (1,800+ plugins for integration)
✅ Automates build, test, and deploy workflows
✅ Works with any language, cloud, or container platform

🧠 Core Jenkins Concepts

Jobs/Pipelines → Define build & deploy workflows.

Agents/Nodes → Where the jobs run (local or remote machines).

Plugins → Extend Jenkins to integrate with GitHub, Docker, Kubernetes, etc.

Declarative Pipelines (Jenkinsfile) → Code-based definition of CI/CD pipelines.

🔧 Example: Simple Jenkins Pipeline

Jenkinsfile:

pipeline {
agent any

stages {
    stage('Build') {
        steps {
            echo 'Building the application...'
        }
    }
    stage('Test') {
        steps {
            echo 'Running tests...'
        }
    }
    stage('Deploy') {
        steps {
            echo 'Deploying application...'
        }
    }
}

}

👉 Save this as Jenkinsfile in your repo. Jenkins will detect it and run the pipeline.

🛠️ DevOps Use Cases

Automate build & test cycles

Trigger deployments on every Git push

Integrate with Docker & Kubernetes for containerized delivery

Run security scans (SonarQube, Trivy) in the pipeline

Enable CI/CD for microservices

⚡ Pro Tips

Use declarative pipelines for readability & maintainability.

Store your Jenkins configuration as code (Jenkinsfile).

Integrate GitHub/GitLab webhooks to auto-trigger builds.

Use agents (Docker, VMs) for scalable execution.

🧪 Hands-on Mini-Lab (Try this!)

1️⃣ Install Jenkins (Docker run or local setup).
2️⃣ Connect Jenkins with your GitHub repo.
3️⃣ Write a Jenkinsfile for build → test → deploy.
4️⃣ Push code and watch the pipeline run automatically.

🎯 Key Takeaway:
Jenkins automates your entire CI/CD lifecycle — from build to deployment. It’s a must-have skill for DevOps engineers who want to streamline delivery pipelines.

🔜 Tomorrow (Day 14):
I’ll explore GitHub Actions — a cloud-native way to build CI/CD pipelines without managing servers. 🚀

🔖 #Jenkins #DevOps #CICD #Automation #SRE #CloudNative