Processing large amounts of data

Why You Need a Stable Superfast Solution for Processing Large Amounts of Data?

As the dependency on data increases, companies are looking for new avenues to process and analyze data for business purposes. The vast troves of data stored in a myriad of databases offer a wealth of opportunities, but only for those that are ready with tools for processing large amounts of data. Superfast and real-time processing of millions and millions of transactions is the key to unlocking business opportunities presented to an organization. However, one thing that needs to be kept in mind before employing a superfast platform is to analyze its ability to handle load, especially during peaks and surges of data influx.

The Essentials for Real-World Real-Time Data Processing

Not only does a business operation have to process and analyze in-flowing data, it also has to analyze security logs. To meet this requirement in the modern world, it is necessary that your processing platform has in-memory processing capabilities and uses MemSQL support for real-time processing of data. But for such a platform to work efficiently, you need it to be stable and have high fault tolerance.

Stabilize Real-Time Processing with a Foolproof Superfast Platform

In the real-world, a failure of hardware is a given. No matter how much you spend on procuring the hardware, the likelihood of hardware failing or developing errors is always there. So, as to ensure that such errors do not compromise your workflow, it is imperative that the platform has the following features:

  1. Horizontal scalability for Big Data – One of the best ways to prevent data loss through server crash is by implementing horizontal scalability. It can be implemented by stacking several servers together, so when the load exceeds on a particular server, it switches the computing of Big Data to the server that is next in line. Load balancers play an important role in switching the server control from one unit to another.
  2. Fault tolerance and self-diagnostic – In case of a malfunction, the server should be able to tolerate the fault and maintain the flow of operation to a certain level. After the malfunction, it should be able to diagnose the root cause of the fault and fix it on its own.
  3. Security – A lot of vital information is stored on the server while processing large amounts of data. This information may include vital customer details, which simply cannot be compromised. To ensure that the details are always secured, it is essential to employ robust security s like AES encryption or other similar preventive measures. The platform should also have a team of specialists who have expertise to analyze security logs and fix any loophole.


In their efforts to find the cheapest and fastest superfast processing vendor, companies often forget to analyze the stability of the platform. This is not the right approach as any malfunctioning of the server may result in vital information loss and hurt a Big Data analytics operation that is using the latest technologies like real-time processing. Hence, it is important to make stability and security as priorities before choosing a platform for processing large amounts of data.

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