The Magic Trick to Find the Right Vendor for Your Large Data Processing Requirement

Data science has come a long way since its inception. Its current scope and future potential has created a need for fast data processing solutions. Especially in telecom and financial sectors, the demand for faster processing capabilities is rising very quickly. This can be attributed to the increasing complexity of technology, which in turn is leading to the creation of intricate software solutions that require higher-than-ever-before computing horse power.

The Problem of Insufficient Processing Power

Big Data, Deep Learning and Business Intelligence (BI) are just a few in the long list of computing-intensive technologies that have massive data processing requirements. Performing these tasks on traditional computer systems is a lot of trouble. The high processing times can add up and undermine the whole point of carrying out such tasks. Just imagine running a deep learning algorithm and waiting for hours in front of the computer screen. A better alternative is to employ faster computers with in-memory processing capabilities. However, such computers don’t come cheap and can be infeasible for most business operations.

How to Find the Right Solution for Large Data Processing?

Finding the right solution for large data processing requirements is what most companies are after. While many are contemplating spending large sums on purchasing expensive hardware, others are looking for cloud-based solutions. But before one goes on a wild goose chase for the fastest and most effective business solution, it is best to understand what one should look for:

  1. In-memory processing – Using RAM for storage purposes allows you to perform in-memory processing, which is amongst the fastest forms of processing in the world. RAM allows for low data fetch time (more than 1000 times faster than a standard HDD). Therefore, you should prioritize in-memory processing to achieve the fastest computing times for your vital tasks.
  2. Scalability – With the advent of deep learning and other big data technologies, the requirements for faster processing are growing at a faster pace. Even in financial and telecom industry, you need state-of-the-art processing to get the job done within the stipulated time limit. You should realize that computing requirements can grow horizontally (more computers required) or vertically (more processing power required) at any time. Hence, you should always choose a partner that is capable of scaling as per your needs.
  3. Fault tolerance – Fault tolerance is the ability of a system to work consistently in spite of experiencing a fault. As your business-critical functions will be performed on the computer systems, you need a partner who can provide robust systems that can work accurately and with the desired efficiency over prolonged periods. Hence, you should always ask your vendor about the fault tolerance capability of computer systems.
  4. Redundancy – Safety of data, during and after processing, is extremely important. Hence, it is best to look for a cloud-based vendor that has multiple points of redundancy (different data centers). This will ensure that your mission-critical data stays safe, in case of natural calamities like earthquakes and tornadoes.

Big Data, Deep Learning and Business Intelligence (BI) are just a few in the long list of computer-intensive techniques with huge demands for data processing. It's a lot of difficulty to perform these duties on traditional computer systems. You should realize that computing requirements can grow horizontally (more computers required) or vertically (more processing power required) at any time. As your business-critical functions will be performed on the computer systems, Hence, you should always choose a partner that is capable of scaling as per your needs. The elevated processing times can add up to the entire level of performing such duties and undermine them. Imagine running an algorithm of deep learning and waiting.

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