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Defining Fast Data Processing

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There is an exponential increase in data sourced and generated from sensors, actuators and other IOT (Internet of Things) enabled devices. Big data is volumes of data generated at incredibly high rates which are not stored or analyzed. Big data is not just getting bigger but getting faster too. The mobile market development during the years and online gaming, where gaming is no longer restricted to static gaming, are classic examples. Analyzing high intake data and applying big data analytics at the time of ingestion is Fast data. Fast data is the application of intelligence to data for faster and smarter responsive solutions.

  Analyzing high intake data and applying big data analytics at the time of ingestion is Fast data.

According to IDC, the digital universe is doubling every two years and is expected to reach 44 zettabytes or 44 trillion gigabytes by 2020. Moreover, IDC predicts enterprise storage compound annual growth rates of more than 50 percent through 2016.

As IOT and high volume environment data sets increase multifold, it becomes crucial to prioritize data sets which are time sensitive and others which are not. It is equally imperative to measure data in terms of megabytes per second or terabytes per day. According to the Data Warehousing Institute, 2 percent of records in a customer file become obsolete each month due to death, bankruptcy, and relocation.

So, why is fast data important?
1. To analyze data and act on data in real time or near real time

2. To gain business insights from huge chunks of stored data

3. To make business decisions on the go.

More than a decade back, it was not possible to analyze petabytes of data using hardware which now is possible with open sources technologies.Back then, interfaces and technology infrastructures were connected system to system. These lacked speed and accuracy of business processes. In order to analyze fast data coming at rapid speed, a steady streaming system capable of identifying essential data from data devices coupled with a data hoard for processing them as fast as they come in are essential.Streaming, in addition to the batch processing is required for timely analysis of data. Also, having a well defined business strategy and knowing the bottleneck of the present architecture makes fast data a reality.

Fast data is clearly the next big thing in data evolution for innovative applications and We offer the best scalable way of leveraging the power of data to your advantage.

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