Edge data centers vs. traditional data centers: What’s the future?
The simplest way to put it is that data centers are facilities where data is stored and processed. While all data centers serve the same purpose, there are different types of these facilities. Edge data centers and traditional data centers have been the talk of the sector for a while now.
Differences between edge and traditional data centers
There are several factors that differentiate these two kinds of data center. Let us begin by defining these two kinds of data centers.
An edge data center is a decentralized facility that is usually located near end-users or devices that generate and consume data. In simpler terms, edge data centers promote local data processing by minimizing the distance that the data needs to travel. TRG Datacenters, a data center in Dallas are perfect examples of EDCs.
On the other hand, traditional data centers are centralized facilities that are built to store, process, and manage large volumes of data and applications of organizations of all sizes. These facilities contain extensive infrastructure like servers, networking equipment, storage systems, and more. The infrastructure and equipment can be stored in one or several centralized locations.
The definitions above touch on the differences between these two types of data centers slightly. Let us look at these differences more in depth:
Location
The most significant difference between traditional and edge centers is about location. Edge centers are located close to end-users or the devices. On the other hand, traditional centers are centralized. Edge centers boast of improved data processing speeds and reduced latency, which are issues with traditional ones, because of their location.
Data processing, governance, and compliance
When it comes to data processing, thanks to proximity, edge data centers boast of faster data processing. This is thanks to the reduced distance that the data has to travel. On the other hand, traditional centers take longer to process data because of longer network distances. On governance, data centers suffer challenges in making sure that data governance and compliance is consistent across all distributed locations. This creates the need for robust monitoring mechanisms and policies to ensure regulatory compliance. Contrastingly, traditional data centers have mostly got the governance issue down. Compliance with data governance frameworks is easier with centralized locations.
Cost and security
When it comes to security, edge data centers do not have it all together. They require robust measures to protect their vast infrastructure in diverse environments. This results in high operational costs of edge centers. The need for distributed infrastructure also plays a role in the higher development and operational costs of edge centers. Contrastingly, traditional ones can implement centralized security protocols. They are also cheaper to operate and deploy because of their centralized nature.
Applications
Edge data centers are best applied for simple processing tasks on small data volumes. For instance, they are commonly used for the Internet of Things. By contrast, traditional data centers are perfect for complex processing tasks or larger data volumes. For instance, they are commonly perfect for big data analytics.
Conclusion: What’s the future?
Edge data centers are characterized by the fact that they are proximal to end-users, are reduced in size, and have the ability to provide services like content delivery and process data faster. Therefore, because of the growing demand for real-time computing and data analysis across several industries, it is safe to say that edge data centers are the future. However, traditional data centers are still not out of the race completely. There are still many instances where they are a better option than edge data centers. Some experts in the market suggest that the future may see companies employing both edge and traditional data centers. These two kinds can be used in tandem. In such scenarios, edge data centers can deal with basic quick processing tasks, while traditional ones can handle ones with more power demands.