This latter trend is evident in the rise of Apache Spark and real-time analytics engines, but it's also clear from the parallel rise of real-time transactional databases (NoSQL). The former is all about lightning-fast data processing, while the latter takes care of equally fast data storage and updates.
The two together combine to "tackle workloads hitherto impossible," as Aerospike vice president Peter Goldmacher told me in an interview.
The machines take over BI
This need for speed is increasingly evident in a new breed of BI. While we normally think of BI as analysis of data by data analysts, DataStax CEO Billy Bosworth said in an interview that, increasingly, machines will take over data analytics.
"'Machine BI'," he says, "is intelligence that has to take place at the processing speed of a machine in order to make a transactional app smarter from transaction to transaction. Human intervention is not possible in this model, and therefore, not a design objective."
In such a world -- say, an online travel application -- the machine must take clickstream data in real time and translate it into relevant offers, layout, and more. There's simply no time for a human to probe the mysteries of user behavior.
As Goldmacher spins it, "IT must capture enormous data sets in order to populate Hadoop and Spark, and the capture mechanism is almost always some sort of low-cost NoSQL environment."
Read More: http://www.infoworld.com/article/2991999/big-data/the-future-of-big-data-is-very-very-fast.html
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