Introduction
As part of the DMI (DevOps Micro Internship) Cohort 2, our team successfully deployed the Spring Petclinic Microservices application on AWS using modern DevOps practices and cloud technologies.
Spring Petclinic is a microservices-based application that consists of multiple independent services working together through service discovery, centralised configuration, API gateways, monitoring, and observability tools.
This project provided valuable hands-on experience in containerization, cloud deployment, collaboration, troubleshooting, and DevOps workflows.
In this article, I will share my experience as the Co-Project Lead and App & Docker Lead, the tasks I completed, challenges I encountered, and the lessons I learned throughout the project.
Project Overview
Our objective was to deploy the Spring Petclinic Microservices application in a cloud environment while implementing DevOps best practices.
The application architecture included:
Config Server
Discovery Server (Eureka)
API Gateway
Customers Service
Visits Service
Vets Service
GenAI Service
Admin Server
We also integrated observability tools, including:
Prometheus
Grafana
Zipkin
These tools helped us monitor system health, application performance, and distributed tracing across services.
My Role in the Team
I served as:
Co-Project Lead
and
App & Docker Lead
My responsibilities included coordinating deployment activities and managing application containerization workflows.
Tasks I Personally Completed
**
**CONT-1: Validate Docker Compose Stack Locally
I validated the complete Docker Compose environment locally to ensure all microservices started correctly and remained healthy before deployment.
This involved:
Testing service startup order
Checking container health status
Validating network communication between services
Verifying application accessibility
CONT-2: Build Docker Images for Linux/AMD64
I built container images for all services targeting the Linux/AMD64 architecture.
This ensured compatibility with the AWS deployment environment.
Example command:
docker buildx build --platform linux/amd64 .
CONT-3: Tag Images with Git SHA
To improve version tracking and deployment consistency, I tagged container images using Git commit hashes.
Example:
docker tag petclinic-api-gateway:latest \
petclinic-api-gateway:
This approach provided traceability and easier rollback capability.
CONT-4: Authenticate Docker to Amazon ECR
I configured Docker authentication with Amazon Elastic Container Registry (ECR).
Example:
aws ecr get-login-password \
| docker login \
--username AWS \
--password-stdin
This enabled secure image publishing to AWS.
CONT-5: Push Images to Amazon ECR
After successful image builds and tagging, I pushed all application images to their respective ECR repositories.
This made the images available for deployment within AWS.
Key Technical Challenge
One of the biggest challenges was ensuring that all services were built successfully and could be deployed consistently.
Containerization can become complex when dealing with multiple services that depend on one another.
We had to carefully validate Docker images, verify service dependencies, and ensure proper communication between components.
Through collaboration, testing, and troubleshooting, we successfully resolved deployment issues and achieved a stable environment.
Technologies Used
During the project, we worked with:
Docker
Docker Compose
Amazon ECR
AWS Cloud Services
Spring Boot
GitHub
Prometheus
Grafana
Zipkin
These tools helped us implement a complete DevOps workflow from development through deployment and monitoring.
What I Learned
This project reinforced several important DevOps concepts:
Containerization
I gained deeper experience building, tagging, and managing Docker images for microservices.
Team Collaboration
Working with teammates taught me the importance of communication, task ownership, and coordination.
Cloud Deployment
I improved my understanding of deploying containerised applications within AWS environments.
Observability
Using Prometheus, Grafana, and Zipkin showed me how monitoring and tracing help identify issues and maintain application reliability.
What I Would Do Differently
If repeating this project, I would:
Automate more deployment tasks using CI/CD pipelines
Implement Infrastructure as Code more extensively
Enhance monitoring dashboards
Improve deployment documentation for faster onboarding
These improvements would increase scalability and operational efficiency.
Final Thoughts
Participating in DMI Cohort 2 was a valuable experience that strengthened both my technical and collaboration skills.
As Co-Project Lead and App & Docker Lead, I gained practical experience in Docker, AWS, container registries, cloud deployment, and observability tools.
Most importantly, I learned how effective teamwork contributes to the success of complex DevOps projects.
If you are interested in gaining real-world DevOps experience, DMI Cohort 3 starts on 27 June 2026.
Apply here:
https://docs.google.com/forms/d/e/1FAIpQLSel7ai7nyb0P1qLW4vEyfB_nEsD4lUF1XG88vmAaFGBOb6hPA/viewform