From Manual Deployments to Automated Delivery: Building a Practical DevOps Pipeline on AWS

Explore how AWS services and DevOps practices can be combined to build an automated CI/CD workflow—from source code and Docker builds to deployment, monitoring, security, and Infrastructure as Code.

Hitesh Padval

Published Oct 1, 2026

Last modified Oct 1, 2026

From Manual Deployments to Automated Delivery: Building a Practical DevOps Pipeline on AWS

Modern applications need more than just reliable infrastructure. Teams must be able to build, test, release, monitor, and improve applications quickly and consistently. This is where DevOps becomes essential.

AWS provides a broad set of services that can be combined to implement a complete DevOps workflow—from source code management to automated deployment and monitoring.

In this article, we will explore how to design a practical AWS-based DevOps pipeline and understand the role of each component.

What Is the Problem?

Consider a development team that deploys an application manually.

A typical process might look like:

Developer writes code.
Code is uploaded manually to a server.
Dependencies are installed manually.
Application is restarted.
Someone verifies whether the deployment worked.
If something goes wrong, the previous version must be restored manually.

This approach can work for small experiments, but it becomes difficult to maintain as the application and team grow.

Common problems include:

Manual deployment errors
Inconsistent environments
Slow release cycles
Difficult rollback procedures
Limited deployment visibility
Configuration mistakes
Lack of automated testing

A DevOps approach addresses these problems by automating the software delivery lifecycle.

Architecture Overview

A simple AWS DevOps architecture can be designed around the following workflow:

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Developer
    |
    v
Source Repository
    |
    v
Build & Test
    |
    v
Container Image
    |
    v
Container Registry
    |
    v
Deployment
    |
    v
Application Infrastructure
    |
    v
Monitoring & Logging

AWS services can be mapped to these stages.

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Git Repository
      |
      v
AWS CodeBuild
      |
      v
Amazon ECR
      |
      v
Amazon ECS / EC2
      |
      v
Application Load Balancer
      |
      v
CloudWatch

The exact services can vary depending on the application architecture, but the fundamental DevOps principles remain the same.

Step 1: Store Application Code in Git

The first step is maintaining application source code in a version-control system.

For example:

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my-devops-project/
├── src/
├── Dockerfile
├── buildspec.yml
└── README.md

Git provides several important capabilities:

Version history
Collaboration
Branching
Code reviews
Change tracking
Rollback to previous versions

A developer might create a feature branch:

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git checkout -b feature/login

After making changes:

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git add .
git commit -m "Add login functionality"
git push origin feature/login

This creates a foundation for Continuous Integration.

Step 2: Continuous Integration

Continuous Integration means automatically validating application changes whenever developers push new code.

The pipeline can perform tasks such as:

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Source Code
     |
     v
Install Dependencies
     |
     v
Run Tests
     |
     v
Build Application
     |
     v
Build Container Image

AWS CodeBuild can be used to automate the build process.

A simplified buildspec.yml might look like:

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version: 0.2

phases:
  pre_build:
    commands:
      - echo "Installing dependencies..."
      - mvn clean install

  build:
    commands:
      - echo "Building application..."
      - docker build -t my-app .

  post_build:
    commands:
      - echo "Build completed successfully."

The build process should fail when tests or required build steps fail.

This prevents broken code from automatically moving further through the delivery pipeline.

Step 3: Containerize the Application

Docker provides a consistent way to package an application and its dependencies.

A simple Dockerfile might look like:

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FROM eclipse-temurin:17-jdk-alpine

WORKDIR /app

COPY target/app.jar app.jar

EXPOSE 8080

CMD ["java", "-jar", "app.jar"]

The image can then be built using:

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docker build -t my-app:1.0 .

And tested locally:

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docker run -d \
  --name my-app \
  -p 8080:8080 \
  my-app:1.0

Containerization helps reduce differences between development, testing, and production environments.

Step 4: Store Images in Amazon ECR

Once the Docker image has been successfully built and tested, it can be pushed to Amazon Elastic Container Registry (Amazon ECR).

A typical workflow is:

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Docker Build
     |
     v
Docker Image
     |
     v
Amazon ECR

Example:

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docker tag my-app:1.0 \
123456789012.dkr.ecr.ap-south-1.amazonaws.com/my-app:1.0

Authenticate Docker with ECR:

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aws ecr get-login-password --region ap-south-1 |
docker login \
--username AWS \
--password-stdin \
123456789012.dkr.ecr.ap-south-1.amazonaws.com

Then push:

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docker push \
123456789012.dkr.ecr.ap-south-1.amazonaws.com/my-app:1.0

Now the application image is available for deployment.

Step 5: Deploy the Application

The container can be deployed using different AWS architectures.

For example:

Option 1 — Amazon EC2

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ALB
 |
 v
EC2
 |
 v
Docker Container

This provides more control over the underlying server.

Option 2 — Amazon ECS

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ALB
 |
 v
ECS Service
 |
 v
ECS Tasks
 |
 v
Containers

Amazon ECS can manage container scheduling and service operation without requiring the team to manually manage every container process.

The choice depends on the application's requirements, operational model, and team expertise.

Step 6: Add a Load Balancer

For production applications, a load balancer can distribute incoming requests across application instances or tasks.

A simplified architecture is:

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                 Internet
                    |
                    v
            Application Load
               Balancer
              /         \
             v           v
          App 1       App 2

If one application instance becomes unavailable, traffic can potentially be directed toward healthy targets.

Health checks are an important part of this architecture.

For example:

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GET /health

200 OK

can indicate that the application is operational.

Step 7: Continuous Deployment

The next step is automatically deploying successfully tested versions.

A deployment pipeline could look like:

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Developer Push
      |
      v
Build
      |
      v
Test
      |
      v
Docker Build
      |
      v
Push to ECR
      |
      v
Deploy
      |
      v
Health Check

This eliminates many manual deployment steps.

A pipeline can also include an approval stage before production:

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Development
     |
     v
Testing
     |
     v
Staging
     |
     v
Manual Approval
     |
     v
Production

This is useful when organizations require additional validation before production deployment.

Step 8: Monitoring With Amazon CloudWatch

Deployment is not the end of DevOps.

After deployment, we need to know whether the application is actually working.

Amazon CloudWatch can help collect and monitor:

Application logs
Infrastructure metrics
CPU utilization
Memory-related metrics where available
Request counts
Errors
Latency
Alarms

For example:

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Application
     |
     v
Logs / Metrics
     |
     v
CloudWatch
     |
     +----> Dashboard
     |
     +----> Alarm
     |
     +----> Notification

A useful production alarm could detect an abnormal increase in HTTP 5xx errors.

Step 9: Infrastructure as Code

Manually creating infrastructure through the AWS console can become difficult to reproduce.

Infrastructure as Code allows infrastructure to be defined in configuration files.

Terraform is a popular example.

A simplified AWS provider configuration might look like:

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

Resources can then be defined as code.

For example:

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resource "aws_instance" "app" {
  ami           = "ami-xxxxxxxx"
  instance_type = "t3.micro"

  tags = {
    Name = "devops-app"
  }
}

The infrastructure can then be managed through:

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terraform init
terraform plan
terraform apply

This makes infrastructure changes easier to review, reproduce, and automate.

Security Should Be Part of DevOps

A good DevOps pipeline should not treat security as an afterthought.

Important practices include:

IAM

Follow the principle of least privilege.

Avoid giving every user or service administrator-level permissions.

Secrets

Do not hard-code credentials inside:

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Dockerfile
Git repository
Application source code
buildspec.yml

Instead, use appropriate AWS security and secrets-management mechanisms.

Security Groups

Allow only required traffic.

For example:

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Internet
   |
   | HTTPS :443
   v
ALB
   |
   | Application Port
   v
Application

Database access should generally be restricted to the application layer rather than exposing the database directly to the internet.

A Complete DevOps Workflow

Putting everything together:

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                   Developer
                       |
                       v
                 Git Repository
                       |
                       v
                  Build & Test
                       |
                       v
                  Docker Build
                       |
                       v
                     ECR
                       |
                       v
                Deployment Stage
                       |
                       v
              ECS / EC2 Application
                       |
                       v
             Application Load Balancer
                       |
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                    Users
                       |
                       v
                  CloudWatch
                 /           \
              Logs           Alarms

Infrastructure can additionally be managed through Terraform:

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Terraform
    |
    +---- VPC
    +---- IAM
    +---- EC2/ECS
    +---- ALB
    +---- Security Groups
    +---- Supporting AWS Resources

What Makes This a DevOps Architecture?

The important part isn't simply using many AWS services.

The real value comes from connecting engineering practices together:

DevOps Practice	Example
Version Control	Git
Continuous Integration	Automated builds and tests
Containerization	Docker
Image Registry	Amazon ECR
Continuous Delivery	Automated deployment pipeline
Infrastructure as Code	Terraform
Monitoring	Amazon CloudWatch
Security	IAM, Security Groups, Secrets
Reliability	Health checks and automated recovery
Collaboration	Pull requests and code reviews

DevOps is therefore more than a collection of tools.

It is a way of making software delivery repeatable, observable, automated, and reliable.

Practical Project Idea

If you are learning AWS and DevOps, this architecture can become a strong hands-on project.

Project

"Automated Containerized Application Deployment on AWS"

Build a small application and implement:

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Git
 ↓
CodeBuild
 ↓
Docker
 ↓
ECR
 ↓
ECS/EC2
 ↓
ALB
 ↓
CloudWatch

Then add Terraform to provision the infrastructure.

Finally, introduce:

Automated testing
Environment separation
Health checks
Logging
Monitoring
IAM least privilege
Deployment rollback strategy

This transforms a simple application into a practical DevOps project.

Lessons Learned

A successful DevOps implementation should focus on reducing unnecessary manual work while increasing reliability and visibility.

The key lessons are:

Automate repetitive processes.
Keep infrastructure reproducible.
Test before deployment.
Use containers to standardize application environments.
Monitor applications after deployment.
Treat security as part of the pipeline.
Keep deployment and infrastructure changes traceable.
Design for failure rather than assuming everything will always work.

Conclusion

AWS provides the building blocks required to implement a complete DevOps lifecycle.

A practical architecture can combine source control, automated builds, Docker, Amazon ECR, ECS or EC2, load balancing, Terraform, IAM, and CloudWatch into one automated workflow.

The goal isn't simply to deploy an application faster.

The goal is to create a system where teams can build, test, deploy, observe, and improve applications consistently.

That is where DevOps and AWS work particularly well together.

Build it. Automate it. Monitor it. Improve it.

#AWS #DevOps #CloudComputing #Docker #Terraform #AWSCloud #CI_CD #AmazonECR #AmazonECS #CloudWatch #InfrastructureAsCode

# aideas-2025 # devops

Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.

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From Manual Deployments to Automated Delivery: Building a Practical DevOps Pipeline on AWS

Hitesh Padval

From Manual Deployments to Automated Delivery: Building a Practical DevOps Pipeline on AWS

What Is the Problem?

Architecture Overview

Step 1: Store Application Code in Git

Step 2: Continuous Integration

Step 3: Containerize the Application

Step 4: Store Images in Amazon ECR

Step 5: Deploy the Application

Option 1 — Amazon EC2

Option 2 — Amazon ECS

Step 6: Add a Load Balancer

Step 7: Continuous Deployment

Step 8: Monitoring With Amazon CloudWatch

Step 9: Infrastructure as Code

Security Should Be Part of DevOps

IAM

Secrets

Security Groups

A Complete DevOps Workflow

What Makes This a DevOps Architecture?

Practical Project Idea

Project

Lessons Learned

Conclusion