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How to do big data on a high performance dedicated server?

Big data refers to the digital data flows resulting from the growing use of the Internet. These are characterized by their large volume, their variety, and the speed at which they are generated. Given their complexity, they cannot be processed by conventional tools. The term therefore also includes the storage, processing and analysis technologies that are adapted to them, including the high performance dedicated server.

Definition and interest of a high performance dedicated server

It is a server placed in a data center and which has all of its physical resources. These are fully allocated to its RAM, storage capacity and computing power. It is opposed to the more traditional virtual server, whose virtualization process consumes part of its resources. Being made available to a single client by the host, it is also opposed to the shared server, whose Noisy Neighbor effect sometimes reduces performance.

Having all of its resources at its disposal, the server is able to provide the power and performance necessary to manage large and complex data. It can thus deploy increased storage and intensive processing solutions, as well as scalable software architectures, which are all uses that may be of interest to your company.

Guarantee and increase your storage capacity via the data center

Many of the advantages of this type of server reside in the data center. On the one hand, it constitutes a guarantee of security for the storage of your data, both in terms of material and environment. On the other hand, it can provide an additional extension to the storage space of your server, which is already significant as such. Data transfer to the data center can be done via a VPN or a solution developed specifically by the server host. It is thus easy to adjust your storage capacity to avoid any loss in the event of a peak in the data flow.

Structure and process your data without interruption using NoSQL and Hadoop

Given the multiplication of data sources, they are produced at high speed, and must be processed by technology that is just as fast. It is imperative to deploy a sufficiently powerful solution to generate information in real time, as the data is received.

In this context, it is, on the one hand, to provide structure to the data collected. These are in fact often lacking, which makes their processing and analysis particularly complex. On the other hand, it is important to ensure intensive data processing, hence the need to use a sufficiently powerful server such as a high performance dedicated server. It should be combined with a constant high throughput processor and adequate data management technology.

The most frequently used tools in this regard are NoSQL and Hadoop. NoSQL is a database management system, while Hadoop is an open source framework. Both distribute the data received within a cluster of machines. In this way, they are able to analyze heterogeneous data. In addition, data processing is not entirely compromised in the event of a machine failure. Finally, the server is able to deploy additional clusters during a peak of data to be processed, which minimizes the risk of loss of information. Thus, it is advisable to opt for a server allowing the use of such technologies.

Often used concurrently, it may be appropriate to combine these tools, as they have affinities with different types of data. Indeed, NoSQL is particularly relevant for the processing of interactive data, including an exchange with the user. As for Hadoop, it has the ability to manage and analyze large-scale data, divided into nodes which are then processed simultaneously by several clusters.

Adjust the management of your data via scalable solutions

This type of server is generally available in several models, which are also customizable by adding or removing tools and options. It is for example possible to add one or more private networks, which are particularly useful from an organizational point of view. The tool is therefore easily modular and allows you to adjust the management of your data at any time to ensure consistency.

Therefore, it is an appropriate technology for the business and project lifecycle. At the launch stage, some needs can be difficult to assess. Opting for a flexible software architecture allowing experimentation is then preferable. The needs identified can then be brought to evolve at the development stage, depending on the difficulties or challenges encountered. It is therefore important to use a solution that is scalable and adapts to new use cases or the possible revision of the objectives set.





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