differences between cloud computing and edge computing

Cloud Computing :-Cloud Computing is a model for delivering information technology services over the internet. Users can now access and use shared pools of reconfigurable computing resources, including as servers, storage, databases, OS, and applications, without worrying about maintaining the underlying infrastructure. The way that organizations and people access and use computing resources has been changed by cloud computing. They can rent computer resources from a cloud provider like Amazon Web Services rather than purchasing and maintaining their own hardware and software. Scalability, cost savings, increased agility, improved reliability and reduced IT costs by leveraging vast computing resources and services accessible over the internet.

Edge Computing :- Edge Computing is a distributed computing architecture that brings computing and data storage closer to the source of data. Data processing takes place at the network’s edge, adjacent to the device that generated the data, as opposed to a central location, such as a data center. Reduced latency and bandwidth needs are desired outcomes of edge computing when transferring large amounts of data to a processing center. Edge computing facilitates real-time decision-making by processing data close to the edge and accelerating data transfer to and from the cloud.

impact data processing and storage

1.Cloud Computing :-

• Data Processing:- Data is processed in centralized data centers. This model excels at handling large-scale computations, such as big data analytics and machine learning, due to its robust and scalable resources.
• Data Storage:- Cloud storage provides virtually unlimited capacity, allowing users to store vast amounts of data with ease. Data stored in the cloud is accessible from anywhere with an internet connection, promoting remote work and global access.

2.Edge Computing:-

• Data Processing:-Data is processed near its source, such as at IoT devices or local edge servers. This reduces latency, enabling real-time data processing and faster response times, which is critical for applications like autonomous vehicles and industrial automation. By processing data locally, edge computing reduces the volume of data that needs to be transmitted to central data centers, minimizing network congestion and related costs.
• Data Storage:- Edge computing systems often use local storage to handle data generated at the edge. This allows for immediate access and processing, which is essential for applications requiring high-speed data access. The local storage can be used for temporary data caching and buffering, with critical data periodically synchronized to the cloud for long-term storage and backup.