#stream processing applications

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React to Customer Interest Faster with Stream Processing Applications

Customers of today have become finicky. With access to Internet at their fingertips at all times (thanks to the smartphones), they are able to analyze products/services faster than ever before. Social media platforms like Facebook and Twitter have transformed into information portals for customers. The wealth of knowledge derived from these platforms sways the decision of millions and shapes the fortunes of brands across the globe. For a business to prosper in the current times, it has become crucial to unearth customer interest and launch product/services accordingly. This is where Big Data analytics and stream processing applications come into the picture.

The Importance of Real Time Data Stream Processing

Real time data stream processing can be put to good use in a number of scenarios. It is particularly useful in situations where data is in motion i.e. it is flowing either in or out of the system. User actions on websites, data generated by sensors and financial trades in stock exchange are prime examples of business situations where data is always flowing. In such cases, Stream Processing Applications can come in handy as they start working on the data as soon as it is generated. Below are some of its important benefits:

Quick insight generation for devising business strategies

A Real Time Data Stream Processing platform generates insights consistently. So, whenever there is a situation developing, companies are able to understand the trends fast and devise strategies accordingly to exploit the market conditions.

Less money spent on storage of data

For traditional data processing systems, large amount of storage space is required. These systems first store the data on HDD or SSD and then process them in batches. Hence, the money spent on storage systems is always high. Considering the modern-day requirements posed by new tech like IoT that generates massive amount of data, these systems are not cost-effective. On the contrary, an in-memory based real time data stream processing computes the data as soon as it arrives thus reducing the storage space requirements. Therefore, it is a better fit for newer technologies and emerging requirements.

Critical Attributes of a Real Time Data Stream Processing Platform

A real time data stream processing platform can only be useful if it serves the present-day requirements of business operations. Have a look at some of its essential attributes:

1.      Fault tolerance – A data processing system is only useful if it stays up for a long duration of time. Therefore, it needs to have high fault tolerance to withstand minor faults and self-diagnostics capabilities to fix the fault when it arises.

2.      Horizontal scalability – The processing requirements may increase at just about any time for a company. Therefore, the real time data stream processing platform should allow for the addition of servers to meet the growing requirements.

3.      Load balancers – When you have several platforms lined up in series, you require load balancer feature to offload the work to a different server when one of the server is under extreme load and likely to malfunction.

Superfastprocessing uses AWS platform and provides state-of-the-art processing solutions for both financial and telecom industries. It has experienced DBAs, software programmers and server experts with long-term experience in the field.

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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.

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.