Edge Computing: how to speed up data processing

5 min

The era of Digital Transformation has generated an enormous amount of data. The information coming from the web and transmitted by the sensors of all the connected devices forces companies to look for a way to skim and manage the data. One such solution is Edge Computing: that is, local management of information. Let’s see how it works and why it’s indispensable for large companies.

Edge Computing Technology Stack

What is Edge Computing? Its definition is much simpler than its pronunciation: it is a computational system that processes information where it originates, as close to the source as possible.

Let’s see what it looks like. The device has a small box shape and is attached to a machine connected to the network. It has software inside that collects the data, analyzes it and processes it quickly locally, and then sends only the final output to the cloud. Where does it get its data from? From sensors installed on machines, smart devices, and anything else that can be connected in an IoT (Internet of Things) environment.

Before this innovation, all the data produced by connected devices in a factory reached the cloud. This resulted in a huge processing delay due to bandwidth and data queuing. In fact, one of the biggest problems with the cloud is information latency.

Today, large companies have several factories in different and often distant geographies. It would become impossible to have all the data produced processed by a single data center, perhaps located miles away from the headquarters. The queue generated as a result would reduce the effectiveness and optimization of the digital processes implemented in the company. This is why Edge Computing is becoming so popular.

The implementation architecture is easily represented with a simple technology stack or layering that shows the layers that are useful to embed Edge Computing in the production reality. The layering can be much more elaborate in complex systems and therefore include other intermediate layers (fog computing). Either way, the end result will be the creation of a process that pushes data from the bottom of the devices to the top of the enterprise.

Stack Edge Computing

1st step: The local network and the connection between physical devices

To better understand how much local data analysis makes a company agile, we need to think about production companies with complex processes and plants located worldwide. Each production unit generates an enormous amount of data which, if processed locally:

  • reduce bandwidth usage
  • accelerate decision-making processes
  • make business more agile

The data crossing local networks have different objectives: they count the pieces made by a machine, they measure lead time, temperature, speed. Each physical device, as needed, constantly produces data. The information produced, in many cases, is used to activate other processes connected to that machine or that production line. Therefore, obtaining information in real-time allows much faster management of processes. In the absence of local processing, data latency may occur, which will result in delays in the response, and the process may generate typos. Here is where Edge Computing becomes indispensable.

The hardware is the data source

The first step of a distributed IT architecture is the data source, that is the hardware: they can be large machines on the production line or smaller devices for personnel safety. When we interconnect them all, by installing software, we create a local edge device network. The nodes of this network are the edge gateways that act as a bridge between the devices and the edge server. The latter is equipped with several interfaces and has greater computing power that allows pre-processing of the data entered into the circle allowed by the IoT.

It is impossible to manage all this only with the cloud and, to be honest, it is not even safe: the risk, when you constantly transmit data to the cloud, is that in the absence of a connection, the data of the analyzed machine could be lost or the service could be interrupted.

2nd step: Edge Computing and decentralized applications

The next step is to create a distributed edge server architecture that processes and stores data from the edge devices locally. In order to do this, greater computing power given by the applications on the Edge server is required. The information will then be processed in real-time and, if useful for the local business process, sent back to several physical devices to implement an action.

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Edge Computing Examples

Edge computing is a transversal software that, as programmable, can adapt to a great variety of industrial realities.

For example, If we take a production company it will be easy to understand what happens when the arrival of information in a process is slowed down. In the production plants, along the assembly line, there may be machines that provide for an autonomous recharge of the raw material. To make this happen there are no resources that control but sensors installed on the machine in question. Their task is to count, for example, the number of pieces used and send the information to the reference Edge. The software gives the number of pieces a refill is made of and, as soon as it is about to run out, it will send a signal to the autonomous driving vehicle (AGV) which will pick up the raw material to take it to the machine.

The benefits in terms of reducing latency are especially evident in systems that exploit computer vision. Forest monitoring cameras, for example, allow immediate action in the event of a possible fire as they detect smoke by analyzing images. If the 24-hour recording must be constantly sent to the cloud, the image upload and analysis time is considerably extended. On the other hand, there are Edge Computing devices, processing the images locally. Only those showing smoke will reach the cloud and trigger an immediate action.

The contexts in which Edge Computing becomes indispensable more than recommended are Smart Grids or Smart Cities where the connected devices generate a multitude of data per second to be managed which, without local processing, would result in an overlap of the actions due to latency and in severe cases the collapse of the connection.


Data with the Edge Computing architecture starts at the bottom with connected devices and, through gateways and edge servers, reaches the infrastructure monitored by the top management. Click To Tweet


3rd step: Data arrives in the hands of executives for analysis

Therefore, an architecture designed this way facilitates the work of executives as it speeds up the arrival of data. Consequently, the responses will be immediate and the predictions more accurate.

Edge Computing offers important benefits to business leaders and management teams, especially in the management of large plants located in different parts of the world.

The advantages of Edge Computing are:

  • scalability – Creating an Edge Computing-based architecture in a plant allows me to customize the optimal solution parameters. It also provides the ability to adapt that application over time.
  • speed – Both in data processing and architecture reproduction in other similar plants but located in different territories. You get the information you need to make predictions or to manage flows in no time.

It also reduces:

  • Latency times – By processing the data locally, I reduce the waiting time for the cloud. In fact, in the absence of such a solution, the software would have to process such a large amount of data that it would create long queues where there is a risk of data aging. This is a high risk, especially for information that needs to be processed in real-time.
  • Bandwidth consumption – having the ability to process information locally, data does not all have to be sent to the server. In this way, if you have many connected devices that generate a large amount of data, with Edge Computing, a quarter of the total will reach the cloud, which will allow you not to slow down or collapse the internet.

Local intelligence allows, in the short and long term, the management of information from physical devices. Although placed at the lowest level of the technology stack, these devices are the coal that powers all business processes, even those that reach the top of the company.

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