<- Back to Work
Case Study

DevOps Cloud Pipeline

An automated containerized workflow using CI/CD pipelines, Docker, and shell scripting targeting cloud server deployments.

GitHub ActionsAWSDockerBash ScriptingLinux
Source Access 🔒
Overview
DevOps Operations
00:0008:0016:0024:00
Uptime / Health
99.9%
Uptime
Deployment< 1 min
AWS2
Automation100%
< 1 minDeployment
100%Automation
2AWS
< 150MBContainer
🕒
< 1 minDeployment Time
🛡️
< 150MBContainer Size
🛡️
100%Automation Success
🛡️
2AWS EC2 Instances
❌

The Challenge

  • ❌Deploying builds manually to cloud servers leads to drift and errors.
  • ❌Oversized container images causing slow pull times on instance reboots.
  • ❌Lack of pre-deployment validation filters letting broken builds pass.
✔️

The Solution

  • ✔️Built automated CI/CD flow using GitHub Actions.
  • ✔️Implemented multi-stage Docker builds reducing image sizes under 150MB.
  • ✔️Configured EC2 container orchestration scripts executing over secure SSH.
  • ✔️Added automated code check filters blocking deployments on build errors.

Key Features

// Feature 01

Multi-stage Docker space

compression

// Feature 02

Automated code lint

& testing filters

// Feature 03

EC2 deployment trigger

SSH scripts

// Feature 04

GitHub Actions pipeline

run integrations

// Feature 05

Secure repository secrets

controls

// Feature 06

Automatic node container

restart policies

Project Gallery

DevOps Cloud Pipeline DashboardLIVE
Primary StatOperations: Active
Speed IndicatorUptime: 99.9%
Operations Monitor Dashboard
Mongoose Collections / Node Cache
KEY / IDTYPESTATUS
sys_core_registryMongoose RecordOnline
auth_jwt_middlewareJWT Guard RouterSecured
data_store_cacheIndex CollectionActive
Database Collections & Cache Records
devops-automation-pipeline.config
1// core/bootstrap.ts
2import { Server } from 'http'
3import express from 'express'
4
5const app = express()
6const port = process.env.PORT || 3000
7
8app.get('/health', (req, res) => {
9  res.json({ status: 'healthy', uptime: process.uptime() })
10})
11
12app.listen(port, () => {
13  console.log(`Server started on port ${port}`)
14})
Source Rules Configuration Code

Project Impact

+ L00.METRIC
+ R00.STAT
+ B50.SYS
+ DATA.SEC
< 1 minDeployment Time
< 150MBContainer Size
100%Automation Success
2AWS EC2 Instances

What I Learned

01

Multi-stage Docker

Multi-stage Docker builds dramatically reduce final production container image sizes.

02

Pre-Deployment Checks

Automating tests before pushing images guarantees broken builds never reach production targets.

03

Pipeline Hardening

Isolating runner tokens and using strict EC2 firewalls protects cloud deployment loops.

+ CTA.01
+ END.SECTION
Open for projects

Have a similar
project in mind?

Let's build a scalable, high-performance solution from architecture to deployment.

Book a Meeting→View All Work→
let's ship it 🚀
4+
Projects
3+
Clients
99%
Uptime