Atharv Gyan

1. What are data centers and what does “hyperscale” mean? Data center: a facility housing compute servers, storage arrays, networking equipment, power distribution, cooling systems, and physical security all to run, store, and transmit digital workloads. They range from small server rooms to massive campuses. Hyperscale: describes a class of very large, standardized, automated data centers operated by major cloud providers or large enterprises. Hyperscale design emphasizes: Massive capacity (thousands to millions of servers across many sites), Standardized racks and modules for predictable deployment, Heavy automation for provisioning, monitoring, and failure handling, Economies of scale that reduce per-unit costs and enable rapid growth. Hyperscale facilities run the backbone of modern cloud services, streaming platforms, search, social media, and large AI model training. 2. Why sustainability is essential for data centers Scale of impact: As cloud use, streaming,...

Edge computing is a decentralized computing model that processes data closer to the source where it is generated (the "edge" of the network), rather than sending it to centralized data centers or cloud servers. This model is particularly beneficial in scenarios involving the Internet of Things (IoT) , where real-time processing, low latency, and bandwidth efficiency are critical. Key Features of Edge Computing Proximity to Data Source : Edge devices, such as IoT sensors or gateways, process and analyze data locally rather than sending it to the cloud.  Reduced Latency : By processing data near its source, edge computing minimizes delays, which is essential for time-sensitive applications like autonomous vehicles or industrial automation. Bandwidth Optimization : Only critical data is sent to the cloud for further analysis or storage, reducing the load on networks. Enhanced Security and Privacy : Since data is processed locally, it reduces the risk of interception during trans...

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():   ...