Microservice Architecture and Distributed Systems using Python, Kubernetes, RabbitMQ, MongoDB, mySQL.
-
Updated
Mar 17, 2023 - Python
Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. These services are owned by small, self-contained teams.
Microservices architectures make applications easier to scale and faster to develop, enabling innovation and accelerating time-to-market for new features.
Microservice Architecture and Distributed Systems using Python, Kubernetes, RabbitMQ, MongoDB, mySQL.
Microservices architecture with Docker and Kubernetes for scalable and containerized application deployment.
Beginner level project to learn kubernetes by hands on experience.
The microservices application SockShop with improved performance and elastic scalability
Python Microservices that enable Continuous Delivery
A backend for a demo Form submission system based on Microservices architecture
This is a microservices-based calculator application that allows users to perform various arithmetic operations. The application is built using Flask and Flask-RESTful and consists of multiple services that are orchestrated using Docker Compose. The frontend of the application is built using HTML and JavaScript.
Simple microservices in Java/Python
Inventory and payment microservices: React, FastApi, Redis, Redis Streams
RabbitMQ connected containerized Microservices using django, flask, react.
Repositorio para pruebas de lanzamiento de aplicación en google cloud con contenedores
Communications between nameko microservices and non-python microservices asynchronously.
HighLow service for managing Highs and Lows
HighLow Email service
A wishbone input module to consume messages from Azure queue storage
SupplyIt users microservice built with flask
This is a repository for the fifth and final project in the AWS Cloud DevOps Engineer Udacity Nanodegree (Capstone Project). Deploys a Deep Learning API Microservice using the Amazon Elastic Kubernetes Service on GPU virtual machines. The deployed containers would automatically utilize the available GPU resources.