March 18

Reasons Why Big Data Companies Are Turning to Edge Computing

The one thing technology experts can accurately predict in the coming years is the continued growth of big data via IoT devices. These data-generating sensors have become the key for operational optimization, as they can detect system vulnerabilities in real time. Here are reasons why certain businesses are choosing edge computing.

What Is Edge Computing?

Edge computing means data processing takes place at or near the data source instead of taking a long journey to the cloud before it's processed. When data has to travel far, packets can get scrambled, which causes latency. When data doesn't have to travel far, it's quicker and easier to access. Edge computing gets its name from facilitating computing at the network edge, which includes endpoints such as smartphones, desktops, notebooks, etc.

So instead of data moving from a sensor to a data center then to the end user, it bypasses the data center and goes directly to the endpoint. Any device that has its own computing capacity can play a role in edge computing.

Main Benefits of Edge Computing

With edge computing, you'll be closer to the data, making latency less likely. Ultimately, it allows you to respond to real-time data with agility. Aside from speed and efficiency, such as real-time data access, major benefits of edge computing include privacy and reliability. By keeping data processing at the network edge instead of transmitting it to the cloud, there's less chance the data will be intercepted by nefarious invaders.

An edge platform gives you potentially higher data computing performance than a cloud-based system. It also provides greater security if each device is configured properly, and its data storage is segmented. The reason edge computing provides better privacy is because you're in control of the data instead of an off-site data center where data can be monitored by unknown individuals.

Edge computing is also particularly useful for companies that need data instantly. Even delays of a few seconds via the cloud can be troublesome to certain companies. They can further be empowered by the concept of an "edge cloud" in which several remote data centers are located near the sources of data generation. Since there are no rules about where to place edge devices on a network as long as they're close to the source, there's room for network flexible configurations.

Industries Using Edge Computing

While various industries have embraced edge computing in recent years, some common purposes exist that attract big data companies. Many large firms within the below sectors have gravitated toward real-time edge devices as part of their digital transformation and support for the IoT revolution.

  • Automotive - Most new cars contain several dozen computing devices that monitor vehicle performance. Autonomous vehicles particularly are equipped with sensors that have computing capabilities. Automated machinery can be used to transport physical items in a factory or monitor street activity to collect data on traffic conditions.
  • Energy and Utilities - Power plants need edge computing due to the massive amount of data they monitor. IoT sensors placed at network edges allow for faster adjustments to improve production processes. Both energy and utility companies have been leaders in adopting smart infrastructures and edge computing.
  • Manufacturing - Assembly machines that make parts may include cameras that track quality assurance analytics. IoT devices can monitor performance for different phases in manufacturing. The data can be monitored at endpoints and sent to the cloud. Adding machine learning software to the equation provides analysts with a wealth of valuable reports at their fingertips.
  • Logistics - Both warehouses and transportation companies have utilized edge computing while integrating it with their cloud models. Warehouses can use edge computing to locate specific products quickly among vast inventories. Trucks and other delivery vehicles are equipped with sensors that monitor vehicle performance and driver behavior. The data can be sent to a database via wireless transmission while triggering alerts to drivers.
  • Agriculture - Farms can now be populated with IoT devices to measure various outdoor growing conditions. They can collect data on temperature, moisture, and soil nutrients without sending it to the cloud. Edge analytics via 5G networks can help farmers more efficiently monitor and predict crop development.
  • Retail - Consumers are learning about the effects of edge computing via virtual experiences and augmented reality at physical stores. Retailers benefit from touchless checkout terminals, for example, that make shopping more convenient. Some retailers have also already started using robots that assist customers with store navigation.

Want to learn more about edge computing? Watch the replay of our Living on the Edge webinar where our speakers break down the complexities of edge computing and provide strategies for successfully deploying edge solutions.

5G Will Enhance Edge Computing

The introduction of 5G helps strengthen edge computing in numerous ways. Have you heard of 5G network slicing? It involves segmenting a shared network for different groups of users, hardware, and software. Essentially, one group's network slice does not affect another slice. Network slicing can be used for various reasons, such as separating different organizational departments.

The wider bandwidth of 5G allows for the expansion of big data and is an important key to a hybrid model that combines cloud and edge computing. This model can potentially turn a local business into a global operation overnight since 5G provides broader data transmission across longer distances.

In a retail store, for instance, different network slices can be used to support different environments. For example, one slice might be for customer experiences with augmented reality while another can be for internal office purposes. Each slice is self-contained and has no impact on other slices. So, if a hacker penetrates a slice, it doesn't mean they can access other slices.

Outlook for Edge Computing

Since edge computing is a viable solution for reducing big data congestion, it will likely be embraced by large entities for years to come. The more IoT devices expand across a business network, the more an organization should consider edge computing. Security can be an issue when selecting edge devices, but with 5G network slicing, you'll have very robust data protection.

Additionally, even though the cloud is a cost-cutting phenomenon, costs can still add up the more data you store in the cloud. In that sense, edge computing provides cost efficiency for big data companies. Non-critical data does not need to be stored on a cloud server. The more non-critical data you keep on edge devices, the more money you can save on cloud storage.

Placing a company's workload near the network edge where data is created has many benefits that save time and money. Data tends to originate from remote equipment rather than the cloud, although the cloud is still very useful for storage and computing. Overall, edge computing is becoming more in-demand by businesses that need to utilize real-time data.


big data, edge applications, edge cloud, edge computing, edge networks, IoT, network slicing

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