The benefits of edge computing in data collection and analysis
In edge computing, competitiveness depends on the speed of data collection and analysis because, as described by the International Data Corporation (IDC), a large network of micro data centers combines processing and storage functions in a distributed manner. This allows critical data to be stored locally, then transmitted to a central data center or cloud storage repository.
Edge computing, with its emphasis on data collection and real-time processing, contributes to the success of intelligent applications that process large amounts of data, overcoming the latency issues of classic cloud solutions.
For example, artificial intelligence or machine learning (AI/ML) tasks, such as image recognition algorithms, can be efficiently performed closer to the data source, eliminating the need to transfer large amounts of information to centralized data centers. Other benefits of edge computing include the ability to perform aggregation and analysis of Big Data in situ, enabling near real-time decision making.
In addition, edge computing reduces the risk of sensitive data exposure because it keeps processing power onsite, enabling companies to enforce security practices or comply with regulatory policies.
Table of Contents:
Edge computing: what it is and how it works
Benefits of edge computing
Edge computing and the Internet of Things
Edge computing and security
Edge computing and enterprises
Cloud and edge computing
Edge computing: what it is and how it works
Research by Intel estimates that by 2025, 75 percent of data will be created outside of central data centers, where most processing occurs today.
Edge computing refers to the practice of checking data from remote sources and performing complex analysis on it. This is an integral part of Big Data analytics, as it helps avoid overloading the capacity of central databases. It also enables more timely decisions due to reduced latency and, in essence, is a method for analyzing data in real time.
Edge computing encompasses all the elements that are located at the outermost boundaries of a network. These include routers, switches, sensors, smart devices and local storage. Its use, for example, occurs with IoT (Internet of Things) devices and deployments, which, having to cope with excessively long latency times and insufficient bandwidth, favor its adoption.
In such cases, edge computing sends critical data, subject to latency, to the cloud after processing it through a smart device located at the point of origin. Alternatively, the data is sent to an intermediate server located at a closer distance.
Critical but less “time sensitive” data can take advantage of the cloud or the company’s data centers to be processed in their complexity.
Some examples in this regard may be Big Data, historical data analytics, long-term storage or everything related to activities aimed at implementing ML algorithm learning (machine learning).
Advantages of edge computing
It is worth mentioning that the term edge was coined by Cisco in 2014 to describe a particular trend that emerged in the development of IT architecture, about its propensity to shift data analysis capabilities from traditional “core” network equipment to devices close to the data source.
The implementation brought by edge computing can be considered from the perspective of its processing and communication capabilities. In fact, data from remote devices are first processed at the edge and then sent to the central database for further analysis.
Alternatively, communication from the edge to the core can be prioritized, allowing real-time monitoring without prior storage or processing. Some systems do both, prioritizing local storage before sending data to a central database.
By performing data analysis locally at the source, latency can be reduced and quick decisions can be made without having to wait for information to travel back and forth over long distances.
Another potential benefit is increased security through decentralization. By moving analytics capabilities away from a single point of vulnerability, edge computing minimizes the impact of security breaches and system outages on business organization processes. This is particularly useful in scenarios where response time is essential, such as emergency services or disaster recovery planning.
Edge computing and the Internet of Things
Data collected by edge computing devices do not require a processor-they can be stored on a server located in the edge. These devices use AI and other advanced features if they are equipped with a processor.
On the other hand, data collected by IoT devices require only basic processing: they send their information to a server for analysis and storage.
Data from edge computing devices can be processed almost in real time or by sending only the necessary data to the cloud. This can be done because an on-premises perimeter server contains critical information needed by applications. Many edge computing devices can be consolidated in the cloud for the purpose of processing and analysis.
IoT devices are not necessarily edge devices but, once connected, are part of many organizations’ edge strategies. Edge computing can provide more processing resources to the edge of an IoT network, reducing communication latency between IoT devices and the central IT networks to which they are connected.
Edge computing technology involves both hardware and software solutions to enable smart devices to operate in harsh and remote environments. These solutions do not require full access to the core network, but use network facilities such as 5G and reduce data latency by minimizing the data sent over the network.
Edge computing and security
IoT edge devices have advantages and disadvantages for network security. On the one hand, more devices mean a larger attack surface. However, edge computing devices offer important security advantages due to their distributed architecture.
By virtue of this, it is easy to implement security protocols that separate compromised devices from the entire network without disrupting all functionality.
Edge computing reduces the amount of data that can be the target of a cyber attack. This is because less data is carried during transit, which reduces the volume of traffic that a malicious user can intercept.
In addition, edge computing reduces the amount of data at risk at any given time due to reduced local data collection. If a device were compromised, only the locally collected data would be affected, not the full volume of it.
The presence of thousands of sensors and devices connected to the Internet poses a serious threat to corporate security. By processing data, locally and offline, edge computing lowers the risk of being exposed to security vulnerabilities. For this reason, companies can store more data without transmitting it over the network.
Edge computing and businesses
Choosing edge computing means you can count on faster and more reliable services at a lower cost. And, at the same time, it also offers a faster and more consistent experience for end users and efficient monitoring capabilities for enterprises and service providers.
By using edge computing, bandwidth limitations can be avoided, transmission delays can be reduced, errors in data transfers can be limited, and the possibilities of their control can be increased.
In addition, the edge enables dynamic and static data caching, short loading times and reduced costs compared to cloud computing.
Cloud and edge computing
As we have seen, edge computing provides several benefits, including low latency, better connectivity, security, and privacy. While this may require a large budget to deal with upfront and maintenance costs, the cloud may be more affordable, even though it entails a whole different response in terms of latency, connectivity, and transmitted data volumes.
The cloud is the best solution for accessing data from any device and location, but because it is a centralized service, it may involve more latency due to the distance between users and data centers.