How Stream Processing Will Revamp The Big Data Landscape

The Big Data industry is going through drastic changes. New technologies are coming into picture as it’s evolving into something bigger and more inventive than ever before. As it turns out, the change was the only way to cope up with the ever-increasing data requirements of the individuals, companies, organizations and group of individuals. The size of data is rising by leaps and bounds. It has rose from certain exabytes to zettabytes and is very likely to be measured in yottabytes in the near future seeing the current demographics.

Over the last couple of years, we have seen a marvelous growth in Data Streaming and its applications. However, with such a massive amount of data, processing at normal pace is just not enough. It must be performed faster and with least possible latency. But seeing the size of data that we have today, it’s impossible to achieve these facets with the available resources for Data Processing. That’s where Stream Processing comes to the rescue. This blog features the broad aspects of Stream Processing and how it can be used to revamp the Big Data industry.  

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What Is Stream Processing?

Stream Processing or “Stream Analytics” is a data processing method that lets you examine and analyze high volumes of data flowing through different devices. You can also extract valuable information from a data stream by using these Stream Analytics tools. This further helps the data engineers to study patterns and trends in the flowing user data which is a key factor required for establishing healthy customer relations. It follows a distributed, fault tolerant architecture which allows it to handle large voluminous data in real-time. The processing is done at a blistering pace and with high precision.

How Stream Processing Helps?

In Stream processing, the data is examined and processed while it’s in motion (i.e flowing from one device to another). This type of processing is far more lucrative than the normal data processing terminology which mainly applies on the static data. As a matter of fact, the value of data recedes exponentially with the passing of time. Thus, the processing of data in motion has its own share of benefits and it’s way more effective than analysing the static data.

The traditional data processing methods require data to be stored first, then it is indexed and then only the processing can be done. This not only takes time but also requires sizable storage medium for handling enormous data. On contrary to this, in stream processing, everything is done while the data is in transit which definitely saves a lot of time and money. Apart from all that, Stream Analytics also provides the best possible way for fraud detection and prevention in real time.