From the Blogosphere
Google Dumps MapReduce
Batch processing systems like MapReduce and Hadoop are too slow for the new era of "realtime big data"
By: Bill McColl
Sep. 11, 2010 11:00 PM
Over the past five years, MapReduce and Hadoop have been widely used for processing big data from the web, both in-house and in the cloud. However, we are now in an era where news, search, marketing, commerce and many other key aspects of the web are becoming much more social, more mobile, and more realtime. In response to these changes, major web companies are realizing that the "big data analytics" that is driving many of their services needs to be radically changed in order to move it into this realtime era. No company sees this more clearly than Google, the company that originally developed the MapReduce/Hadoop approach to processing big data.
Another article notes
We are now at the start of a new era in the big data world. Increasingly, big data apps will need to be realtime. For example, a recent list of "Ten Hadoop-able Problems" contains the following examples of big data problems that can be tackled with MapReduce/Hadoop:
In each case, it is clear that these are big data problems where the ability to deliver the results of the analytics in realtime would increase the value of that analytics enormously.
As we move beyond MapReduce and Hadoop, into this new era of "realtime big data", where analytics apps are "always-on" and run continuously, we can expect to see a major wave of software innovation, with many exciting new realtime apps from developers in areas such as marketing intelligence, social commerce, social enterprise, and the mobile web.
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