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Azure Data Lake

What is Edge Computing? Its Importance and applications

What does Edge Computing means?

Edge computing is defined as a form of computing performed on-site or close to a specific data source to reduce the requirement for data to be processed in a remote data center. Edge computing provides businesses with a quicker, more effective means to process data using enterprise-grade applications. In the past, edge points produced enormous data volumes that were frequently discarded. With the advent of mobile computing and the Internet of Things (IoT), IT architecture can now be decentralized, allowing businesses to access near real-time insights with reduced latency and bandwidth requirements for cloud servers - all while enhancing security for sensitive data.

Why is Edge Computing Important?

The most sensitive data is already processed and vital systems that must operate securely and consistently are powered by a large portion of today's computing, which already takes place at the edge in locations like factories, retail stores, and hospitals. Low latency, network-free solutions are necessary for these locations. Edge has the ability to transform an organization across all sectors and functions, from consumer engagement and marketing to production and back-office operations. Edge supports proactive and adaptable business processes, frequently in real-time, resulting in improved user experiences. Edge enables companies to integrate the digital and real worlds.

Edge computing benefits and applications

Edge can be used to drive quick decisions and enhance user experiences by maximizing relevance at every touchpoint. The cloud backbone now enables edge computing to contribute to the development of new insights and experiences.

A few common benefits of edge computing are listed below:

Quick response: Data migration takes time. In some use cases, such as telesurgery or self-driving cars, there isn't enough time to wait for data to travel back and forth from the cloud. Edge computing is helpful in these situations, where real-time or highly quick results are required.

Massive data volume: The cloud is capable of handling extremely large data volumes, but there are considerable transmission costs and physical network capacity constraints. Processing the data at the edge can prove highly beneficial in such circumstances.

Privacy: Users may choose to preserve control of sensitive data locally rather than transmitting it to the cloud.

Remote Areas: Some use cases fall under the category of "remote" in terms of connectivity, whether they are actually remote (such as an oil drilling or offshore platform) or practically remote (involving mobile or transportation-related scenarios utilizing edge).

Cost sensitivity: Processing data entails varying cost profiles across the cloud continuum, which can be optimized to reduce overall system costs.

Autonomous operations: Users may require end-to-end processing within the local environment to maintain operations if access to the cloud is not possible or is expected to be intermittent or unstable.

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