Human Resources Management Project

The Human Resources Management Project is a comprehensive backend-focused application designed to optimize HR management processes. The primary goal of the project is to enable effective handling of employee data, leave requests, performance evaluations, and other administrative tasks in an organization. Leveraging a microservices architecture, the system ensures high scalability, modularity, and maintainability.

Microservices

01

ConfigServer

The ConfigServer is responsible for centralized configuration management in a microservices architecture. It allows all microservices to dynamically retrieve their configuration properties from a single source, reducing complexity and improving maintainability. Real-time updates can be applied to configurations without restarting services, enabling faster adaptation to changes.

02

AuthMicroService

Manages user authentication and authorization processes, including user logins, password changes, and permission updates.

04

UserMicroService

Handles processes related to employees, such as user profiles, leave requests, expense management, and performance evaluations.

03

MailMicroService

The MailMicroService handles email notifications for processes like user registration and account verification. It uses RabbitMQ-based messaging to process email tasks asynchronously, ensuring high performance and responsiveness. Emails are sent using dynamic templates, allowing for personalized and professional communication. The service also includes error handling and retry mechanisms to improve delivery reliability.

Technologies Used

Key Features

01

Authentication and Authorization

02

Employee Management

03

Configurable and Modular Design

04

Email Notification System

05

Expense and Permission Management

06

Microservices Communication

The project aims to provide a flexible and scalable architecture that enables companies to effectively manage their human resources processes. With this architecture, new features can be easily added, and system performance can be improved in a scalable manner.

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