The benefits of edge computing in data collection and analysis
In edge of computing, competitiveness depends on the speed of data collection and analysis because, as described by theInternational Data Corporation (IDC), a large network of micro data centers combines computing and storage functions in a distributed manner. This allows you to store critical data locally, then stream it 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 problems of classic cloud solutions.
For example, artificial intelligence or machine learning (AI/ML) tasks, such as image recognition algorithms, can efficiently run closer to the data source, eliminating the need to transfer large amounts of information to data centers centralized. Other benefits of edge computing include the ability to perform big data aggregation and analysis on site, enabling near real-time decision making.
Additionally, edge computing reduces the risk of sensitive data being exposed by keeping processing power on site, allowing companies to enforce security practices or comply with regulatory policies.
Edge computing: what it is and how it works
A search for intel estimates that, by 2025, 75% of data will be created outside central data centers, where most of the processing takes place today.
Edge computing refers to the practice of controlling 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 capacities of central databases. Furthermore, thanks to the reduced latency, it allows for more timely decisions and, in essence, represents a method for analyzing data in real time.
Edge computing encompasses all elements found at the outermost edges of a network. These include routers, switches, sensors, smart devices and local storage. The usagefor example, occurs with IoT (Internet of Things) devices and implementations which, having to cope with too long latency times and insufficient bandwidth, favor their adoptionand.
In such cases, edge computing sends critical data, prone 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 use the cloud or company data centers to be processed in all its complexity.
Some examples in this regard can be represented by Big Data, the analysis of historical data, long-term storage or everything concerning the activities aimed at implementing the learning of ML algorithms (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 has emerged in the development of IT architecture, around its propensity to move data analysis capabilities from traditional “core” network equipment to devices close to the data source.
The implementation brought about by edge computing can be considered in terms of its processing and communication capabilities. In fact, data from remote devices is 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, enabling real-time monitoring without prior archiving or processing. Some systems do both, prioritizing local storage before sending the data to a central database.
By doing data analysis locally at the source, it is possible reduce latency and make quick decisions without having to wait for information to travel back and forth over long distances.
Another potential benefit is a 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 especially useful in scenarios where response time is of the essence, such as emergency services or disaster recovery planning.
Edge computing and the Internet of Things
The data collected by edge computing devices does not require a processor: it can be stored on a server located at 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 requires only basic processing: they send their information to a server for analysis and storage.
Data from edge computing devices can be processed in near real-time or by sending only the necessary data to the cloud. This can be done because an on-premises Edge Server contains critical information needed by applications. Many edge computing devices can be consolidated in the cloud for processing and analytics.
IoT devices aren’t necessarily edge devices but, once connected, they are part of many organizations’ edge strategies. Edge computing can deliver more computing resources to the edge of an IoT network, reducing the latency of communications 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 instead use network facilities such as 5G and reduce data latency by limiting the data sent over the network to a minimum.
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 benefits due to their distributed architecture.
Because of this, it’s 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 targeted by a cyber attack. This is because less data is carried in transit, which reduces the volume of traffic that an attacker can intercept.
Moreover, edge computing reduces the amount of data at risk at any one 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 represents a serious threat to corporate security. By processing data, locally and offline, edge computing lowers the risk of being exposed to security vulnerabilities. Because of this, companies can store more data without transmitting it over the network.
Edge computing and enterprise
Choosing edge computing means being able to 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 businesses and service providers.
By using edge computing you can avoid bandwidth limitations, reduce transmission delays, limit errors in data transfers and increase the possibilities of their control.
The edge also enables dynamic and static data caching, short load times, and lower costs than cloud computing.
Cloud and edge computing
As we have seen, edge computing provides several benefits, including low latency, better connectivity, security and privacy. While on the one hand this could require a large budget to meet initial and maintenance costs, on the other hand the cloud could be more accessible, even if it involves a completely different response in terms of latency, connectivity and volumes of data transmitted.
The cloud is the best solution for accessing data from any device and place but, being a centralized service, it can imply greater latency due to the distance between users and data centers.