Java EE and Microservices

Modular Monolith Java application with DDD

Modular Monolith architecture demonstration with Spring Modulith and DDD

Case Study - Sample Book Delivery App (Spring Boot, Spring Security , Mysql, JUnit, Integration Test, Docker, Test Container, AOP, CI/CD, Prometheus, Grafana, Github Actions)

Case Study : Parking Lot (Spring Boot, Java 17, Maven, JPA, Spring Security (JWT) Actuator, MySql, JUnit, Integration Test, Docker, Test Container, AOP, CI/CD, Prometheus, Grafana, Github Actions, Liquibase)

Spring Boot with Spring Security (Role Permission) (Spring Boot, Java 21, Spring Security, Docker, Maven, JUnit Test, Integration Test, Test Container, MySql, Github Actions, Spring Open Api)

Simple Banking App (Spring Boot, Gradle, JUnit, Integration Test, Postgresql, Prometheus, Grafana, Github Actions, Postman)

Spring Boot Security Example (Spring Boot, Java 17, Spring Security, Docker, Maven, JUnit Test, Integration Test, Test Container, MySql)

This application is made to showcase DDD-Lite within a modular monolith application. The motivation behind this is to show that a monolith can be implemented consistently and easily migrated to a microservice architecture ,if needed, some time in the future.

Case Study - Openpayd Foreign Exchange Example (Spring Boot, Java 21, Docker, Maven, JUnit Test, Integration Test, In-Memory Database (H2 Database), Github Actions, Spring Open Api, Mapstruct, Spring Cache, Resillience4j (Rate Limiter))

Hexagonal modular pattern for agile microservices evolution

Monolith

Este proyecto se trata de un monolito hecho en Java, para la administración de una Cafetería. Posee una arquitectura MVC.

Everyday is getting more and more popular and, sometimes, worthy and useful -> split a monolith into microservices. In this repo I will show an example of how to split a monolith into microservices using the Strangler Fig and Branch By Abstraction patterns.

A basic JHipster generated monolith application to catalogue your dinosaurs.

BACHELOR THESIS. Detect wind turbine errors using a hybrid model. Utilize ML, DL, edge computing. Dataset, neural network optimized with genetic algorithms. Low computational power ML models. Data is stored in the cloud. Turbine status via secure APIs. Password encryption. GRASP, SOLID

System that uses machine learning algorithms to test various aspects of performance - CPU usage, waiting time, memory consumption, cashe usage, idle time, heap space etc.

Finance Application Back End Repo Monolith Architecture

Starter Spring Boot Rest Api Monolith Application