Edge Vs Cloud: Which Is Better For Data Analytics

Once the basis of storylines for science fiction movies, Artificial Intelligence (AI) now has useful applications that are transforming the way businesses are conducted. Developers are examining ways to merge AI with everyday devices to help companies run businesses. 

In this scenario, cloud computing plays a significant role in making the best decisions possible. A cloud-based platform enables developers to rapidly build, deploy and manage their applications, such as serving as a data platform for applications, build an app to scale and support millions of users and interactions, and more. It can store large amounts of data and perform analytics, create powerful visualisations, and more. 

Then there is edge computing, which means that applications, services, and analytical processing of data is done outside of a centralised data centre and closer to end-users. Edge computing closely aligns the Internet of Things. It is a step back from the trendy cloud model of computing where all the exciting bits happen in data centres. Instead of using local resources to collect data and send it to the cloud, part of the processing takes place on the local resources themselves. 

We all know the value of cloud computing for data analytics, and how extensively it is used across companies. On the other hand, sometimes businesses can run into the problem of having to collect transport and analyse all that data.  

Let’s say you’ve got some internet-connected sensors in your warehouse, and these are sending lots of data back to some servers. When the data is transmitted to the remote cloud server, you can perform complex machine learning algorithms to try and predict maintenance needs for the warehouse. All these meaningful analytics are then sent to a dashboard on your personal computer where you can determine which actions to take next, all from the comfort of your office or home. 

This is the power of cloud computing; however, as you begin to scale up operations at the warehouse, you might start to run into physical limitations in your network bandwidth, and latency issues. 

Instead of transmitting your data across the country when you upload to the cloud, you can also do data processing at the edge, like a smart camera with facial recognition where sending tons of data to an Amazon data centre might not be so convenient. 

Edge computing attempts to bridge the gap by having that server more local, sometimes even on the device itself. This solves the latency problem at the cost of the sheer processing power you get via the cloud.