github.com/naufalmohamed/laplunch
Disclaimer: This blog post was created with the assistance of AI. While the content reflects my personal experiences and insights, the AI was used to enhance the writing process and ensure clarity.
Introduction
In today’s tech landscape, developing a robust and scalable web application can be a challenging yet rewarding endeavor. Recently, as part of a corporate training program, our team embarked on a group project to create a comprehensive food delivery web app. The project not only provided hands-on experience with modern technologies but also emphasized the importance of teamwork and collaborative problem-solving. Here’s a look at how we brought our vision to life and the key contributions made by each team member.
Project Overview
Our food delivery web app was designed to streamline the process of ordering food online. Leveraging state-of-the-art technologies like Docker, MongoDB, Neo4j, and RabbitMQ, we built a system that is both efficient and scalable. The application architecture consists of several microservices that work in harmony to deliver a seamless user experience.
My Contributions
As a member of the team, I focused on three critical areas of the project: deployment using Docker, developing a recommendation system with Neo4j, and implementing a mail notification system.
Deployment with Docker
One of the cornerstones of our project was ensuring that all components of our application were consistently and efficiently deployed. I took on the responsibility of deploying the app using Docker, which allowed us to containerize our services and manage dependencies seamlessly.
Using Docker Compose, I orchestrated the building and running of multiple services. This included setting up the Eureka server for service discovery, the API Gateway for routing, MongoDB and Neo4j databases, and RabbitMQ for messaging. Docker provided us with a reliable and reproducible environment, making it easier to manage different services and configurations.
Recommendation System with Neo4j
A standout feature of our app is its recommendation system, which leverages Neo4j, a powerful graph database. The recommendation system enhances the user experience by suggesting menu items based on user preferences and order history. Neo4j’s graph-based approach allowed us to model complex relationships between users and menu items effectively.
I designed and implemented the recommendation algorithms, which use graph queries to identify and suggest relevant items. This system not only improves user satisfaction but also drives engagement by personalizing the food delivery experience.
Mail Notification System
To keep users informed about their orders and provide timely updates, I also developed a mail notification system. This feature ensures that users receive notifications about order confirmations, status updates, and promotional offers.
Integrating this system involved setting up an email service that interacts with our application to send automated messages. By doing so, we were able to enhance user communication and keep our customers engaged with relevant information.
Team Effort and Collaboration
This project was a true testament to the power of teamwork. While I focused on deployment, the recommendation system, and email notifications, other team members handled different aspects of the application. We collaborated closely to ensure that each component of the app integrated smoothly and met the project’s requirements.
The experience of working on this project taught us valuable lessons in both technical skills and collaborative problem-solving. It also highlighted the importance of clear communication and coordination within a team.
Conclusion
Our food delivery web app project was an exciting and challenging endeavor that allowed us to apply and expand our technical skills. By leveraging Docker for deployment, Neo4j for recommendations, and a custom mail notification system, we created a dynamic and user-friendly application.
If you’re interested in exploring the code or contributing to the project, feel free to reach out or check out our repository. We’re always open to feedback and contributions that can help improve the app.