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What is Edge Computing? Your Questions Answered!

If this article popped up on your social media feed and you thought, what in the world is “Edge Computing?!” Don’t worry, you aren’t alone. Here’s the scoop:

Edge computing allows businesses to safely “move” and analyze heavy rivers of data, much faster and using less bandwidth. Thanks largely to the growth of Internet of Things (IoT) devices and 5G technology, more and more industries are adopting edge computing. Interest in edge computing continues to rise, particularly since faster processes are required for artificial intelligence, robotics, self-driving cars, remote monitoring, and more.

All of these reasons make it an opportune time to ask the question, what exactly is the edge computing ecosystem?

What is edge computing?

Edge computing is a distribution approach for a pool of resources across a large number of locations. These resources can include both warehouses and databases. Edge computing operates under the assumption that is more efficient to gather, store, and process data as close as possible to the user or at the point of generation, as opposed to a single centralized location.

Hence, the ‘edge’ of any system, network, or architecture is closest to a company’s data or users.

What is an example of an edge computing ecosystem?

Most edge computing ecosystems are built around the need to process data locally in real time, when data transmission to a central location is not feasible. Self-driving cars are an example of an edge computing ecosystem. Each car is connected to a central warehouse of data, allowing it to receive updates of the most efficient routes with the fewest obstacles (e.g. traffic, construction, or accidents). Each car is equipped with sensors to generate a live data stream. This data is stored and processed in real time by each car’s local data analytic processes to inform decisions, prevent breakdowns, and improve operations.

Processing the car’s data at the point of generation is faster and more accurate, enabling the car to react to dangerous road conditions at a moment’s notice.

Who uses edge computing?

Manufacturing, oil and gas, retail, and healthcare manufacturers are among the earliest adopters of edge computing. Edge computing has been used to improve the functionality of smart-watches, manufacturing plants, remote monitoring (oil and gas), electronic health monitoring devices, cloud gaming, traffic management, and more.

Why does edge computing matter?

For companies, services are more stable and costs are reduced by shortening the communication distance between data extraction, data transfer, and data loading. For users, edge computing provides faster, more customizable, and more consistent experiences. Service providers are available to give apps with real-time monitoring, limit service failures, and better control sensitive data.

What are some benefits of edge computing?

Some of the most obvious benefits of edge computing include:

  • Real-time or faster data processing and analysis
  • Lower data management costs compared to data center networks
  • Less network traffic and improved application performance
  • More reliable data analyses and results
  • Decreased load time and increased capability
  • On-site data analytics and aggregation - reducing risk

What are some downsides to edge computing?

Although the system or architecture complexity increases with additional data processes and storage, it is generally easier to direct all data to a central location. In terms of complexity, the increased number of data sites (hardware or software) produced by edge computing requires more skilled IT employees, and increased infrastructure to fix major problems. Additionally, more planning and strategy are required to efficiently execute an edge computing ecosystem, as more sites also require more security.

Conclusion: We are excited for the future of edge computing!

Edge computing has gained popularity thanks to the need to collect, store, and analyze data in real time. The future of technology depends on innovations like edge computing to handle the need for customizable, unique, and modular use cases and experiences.

Topics : Articles, Artificial Intelligence, data, Edge Computing, Internet of Things, Technology

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