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Real-Time Analytics: Ready For Its Close-Up?
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Hoganator
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Hoganator,
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2/10/2014 | 5:32:48 PM
Real-Time Analytics
Hadoop opened people's eyes to the value found in large pools of data, but the delay imposed by batch processing dilutes that value considerably. Real-time (or near real-time) analytics are far more valuable. Stream processors (like Twitter Storm, Linkedin Samza, Yahoo S4, Amazon Kinesis, Microsoft StreamInsight, etc.) provide the ability to process/filter/count/transform the data in real-time but the window of data visibility is limited and fleeting. Think of it as looking out the side window of a car doing 100MPH. You can count things and do limited "processing" but only in looking back do you get the true lay of the land. Stream processors require persistence, to enable historical analytics. The problem is: (a) in-memory systems are far to expensive given the huge data volume; and (b) disk-based systems cannot keep up with the flood of fast data, causing an impedance mismatch. Further, like Hadoop, in order to spur corporate adoption it requires SQL support. Hadoop+YARN is interesting, but the underlying large grain file system is incompatible to smaller "block-sized" data found in streams.

Resources: 

http://scaledb.blogspot.com/2014/01/stream-processors-and-dbms-persistence.html

http://scaledb.com/high-velocity-data.php

 

 

 


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