Big Data Hardware: all about data analysis hardware

Big Data is not only based on software, but also on hardware. Through this feature, find out everything you need to know about Big Data hardware and the trends that are shaping the future of the industry .

The collection, storage, processing and analysis of data are the main steps of the Big Data. To perform these tasks, Data Scientists and other professionals use a variety of software platforms.

However, Big Data also induces hardware requirements. Powerful hardware, optimized for processing huge volumes of information, is indispensable. Find out everything you need to know about Big Data hardware.

Big Data requires big hardware.

Companies of all sizes want to exploit Big Data. However, many SMEs do not realize hardware requirements that data analysis involves.

Even a small application generates huge volumes of data that will have to be stored. The Cloud is generally not sufficient, and investments in hardware are essential. Especially in hard disks and RAM strips.

Another reason why companies underestimate the infrastructure needs brought about by Big Data is because they do not always understand what this technology really is. The more data an organization collects, the more storage it will need.

In the past, information was stored on databases on a single server. This is no longer possible today, as a single server is no longer sufficient to cope with the colossal volume of data. Needs also depend on the nature of the data collected.

In addition to servers, desktops also need to be updated. For this reason, running the data analysis programs leads to latency on a machine equipped with a hard disk of only 500GB and 4GB of RAM.

Therefore, small businesses should only not focus solely on data collection and analysis, but also take hardware requirements into account.

Generally speaking, Hardware is much more expensive than software. to buy and maintain. That’s why many companies are turning to the Cloud, eliminating maintenance costs. However, in many cases, an in-house data center is also essential.

Requirements also depend on how the data collected is used. In some cases, a single data center is not enough and the data must be stored on individual interconnected nodes.

It is therefore difficult to estimate the cost of this investments, knowing that it depends on the specific needs of each organization. In general, it is the data storage capacity that is expensive rather than the processing power.

Massive data is often stored on multiple optimized nodes, and this solution is efficient, but very expensive. Many companies are turning to SSDs instead of HDDs for faster access to data. Again, the cost is high.

It is therefore important for companies planning to enter the Big Data business not to underestimate the hardware requirements involved. It is the need to invest in adequate infrastructurewith sufficient storage capacity on the servers and processing power on the computers where the scans will be performed.

These expenses must be plannedto avoid unpleasant surprises and unnecessary expenses. Define your needs, estimate the costs, conduct research in order to carry out your project in an economical way…

Data Capture Hardware

The Big Data starts with data collection. Therefore, all devices that can capture information can be considered as part of the Big Data “hardware”.

Examples include smartphones, cameras, carsThese include watches, connected objects, security systems, motion sensors and credit card terminals.

All these devices allow to collect information on daily activitiesThe information is based on the habits or preferences of their users. In reality, the number of devices capturing data seems unlimited today.

Big Data Hardware must have several characteristics to allow for proper data collection. It must allow data to be captured accurately. For example, the connected thermostats must be properly calibrated and the cameras must offer high image resolution.

If not, if the data is of poor qualitythe results of the analyses will be biased. There is therefore a risk of missing important opportunities. If you plan to take advantage of the benefits of Big Data, be sure to choose the best data capture equipment.

Ideally, the data should be transmitted in real time to the Cloud where they will be analyzed. This ensures that the analysis results are not already obsolete by the time they are produced. For example, Data-Driven advertising targeting will not work if the consumer is no longer interested in the product offered.

In addition, it is important that the Big Data hardware is compatible with all analytical systems and all cloud platforms. Technology is evolving at a rapid pace, and users will inevitably change platforms or use new ones over time.

Also, the hardware must be compatible with different standard protocols communication devices such as IEEE 802.11, Z-Wave, ZigBee, VESA, MP4, USB or Bluetooth. Easy-to-use and well-documented APIs should also allow developers to quickly integrate a cloud solution with the hardware.

Big Data Hardware Industry Trends

In recent years, several trends have been shaking up the Big Data hardware industry. First and foremost, Cloud servers are more accessible than ever before. This technology allows data processing tasks to be performed on remote servers.

It offers unbelievable elasticity and stabilityand allows for instant deployment and cost control. Physical servers are increasingly being abandoned for the cloud.

By the way, SSDs (solid-state drives) are becoming more and more popular.. These cell-based storage devices provide instant access to data, unlike traditional hard disk drives that use the rotation and movement of a needle. They are also more likely to be damaged by a mechanical problem. SSDs have become a standard on servers as they meet the new needs of Big Data.

The There is also a trend towards artificial intelligenceWith the emergence of dedicated processors such as the Intel Xeon processors, data analysis can be accelerated and more accurate results can be delivered. AI also enables the automation of analytical tasks.

In the mobile industry, hardware also embraces artificial intelligence. The smartphone chips such as Qualcomm’s Snapdragon now leverage AI to store and process data locally.

Over the coming years, these trends will continue. L’Artificial Intelligence and the Cloud are undoubtedly the two technologies with the greatest impact on Big Data hardware and computing in general, while SSDs are now replacing hard disks.

Why does Big Data need a hardware revolution?

In the field of computer science, innovation often seems to be focused on software. New programs are emerging to monitor and measure our health status, artificial intelligence surpasses humans in board games.

Unfortunately, material innovation seems to be too often neglected. In order to rectify this, in 2018, the Semiconductor Research Corporation (SRC) consortium announced the opening of six university centres.

This consortium of companies, researchers and government agencies aims to chart the future of the semiconductor industry. This global initiativeand several companies from around the world have joined the American consortium.

These include the case of South Korean giant Samsung.. Major chip manufacturers are looking to reclaim their territory from software giants such as Google who are venturing into the field of AI hardware and Big Data.

Their objective is initiate a major transformation of the industrywhich may mark the first architectural revolution since the beginnings of computing. Indeed, the traditional von Neumann architecture, which consists of separating data storage components from those dedicated to data processing, no longer meets modern needs.

The information transfers between components require time and power, and hinder performance. To analyze huge datasets using AI, researchers in astronomy, physics, neuroscience, genetics, etc. need new kinds of computers that go beyond the limits of this architecture.

For several decades, advances in computer hardware seem to have been confined to reduce the size of the components and double the number of transistors on a chip every two years based on Gordon Moore’s predictions. The fundamental design, however, is not evolving and places a limit on the possibilities.

The solution could be to “merge” memory and processing units of data. However, performing calculation tasks on a single memory unit is a real technical challenge.

An alternative can be seen in the searches conducted by Google AlphaGo. The firm has produced a new type of component called “TPU (Tensor Processing Unit). These chips are distinguished by their architecture allowing many more operations to be performed simultaneously.

This approach to parallel processing increases the speed and efficiency of the most intensive calculations. In addition, the “rough computing” strategyThe use of a larger margin of error for the machine could also be beneficial.

It is precisely this method that has made it possible to drastically reduce energy consumption from programs like AlphaGo. Such a hardware upheaval could democratize AI.

Another lead to follow is that of neuromorphic computingwith chips inspired by the human brain, which to this day remains the most energy-efficient processor. This technology simulates the communication and processing carried out by our nervous system.

In order to explore these different possibilities, the SRC seeks to encourage hardware designers to move forward. Through the Joint University Microelectronics Program, the consortium wants to stimulate the development of a new architecture.

For example, the centre established at Purdue University in West Lafayette, Indiana, focuses on research in neuromorphic computing. The centre at the University of Virginia in Charlottesville, Virginia, is looking for new ways to to exploit the memory of computers for extra processing power.

The final goal remains the same: to create an IT architecture capable of supporting the storage and processing of Big Data. As the volume of data increases, the need for a hardware revolution will intensify .

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