A guide on building and deploying microservices using Python, including best practices, tools, and frameworks like Flask and FastAPI. Implementing Microservices with Python Microservices architecture has become a popular approach for building scalable and maintainable applications. It involves breaking down a large application into smaller, independent services that can be developed, deployed, and scaled independently. Python, with its rich ecosystem of libraries and frameworks, is an excellent choice for implementing microservices. This guide will explore how to build and deploy microservices using Python, with a focus on Flask and FastAPI. Why Microservices? Microservices offer several advantages over monolithic architectures: Scalability: Each service can be scaled independently based on its load. Maintainability: Smaller codebases are easier to understand, test, and maintain. Deployment: Services can be deployed independently, allowing for more frequent and safer deployments. Tec

Decorators are a powerful feature in Python that allows programmers to modify the behavior of functions or methods. They provide a concise way to add functionality to existing code without modifying it. In this guide, we'll explore decorators in Python, understand their syntax, and demonstrate their practical usage through examples. Understanding Decorators: Decorators in Python are functions that wrap other functions or methods, allowing you to execute code before and after the wrapped function runs. They are typically denoted by the '@' symbol followed by the decorator name, placed above the function definition. Syntax: ``` python @decorator def function():     pass ``` Decorator Functions: A decorator function takes another function as an argument, performs some processing, and returns a new function or modifies the existing one. This enables you to extend the behavior of functions dynamically. Example: ``` python def my_decorator(func):     def wrapper():         print(