Hadoop Emerging as Dominant Big Data Analytics Platform: 10 Reasons Why

 
 
By Chris Preimesberger  |  Posted 2013-02-06 Email Print this article Print
 
 
 
 
 
 
 
 

Big data batch-processing platform Apache Hadoop has gained significant footing during the last three years as two key things have happened: Web 2.0 companies increased operational scale and traditional market segment deployments shifted from early pilot applications to large production deployments. IT advances in networking, storage capacities and integration services have made Hadoop a natural fit for a wide range of enterprises and their applications. As they look ahead to 2013, a number of IT experts believe Hadoop is poised to firmly establish its dominance in big data analytics with the addition of even more capabilities. Among these are Hadoop's ever-increasing set of APIs for analytics (including MapReduce, query languages and database access), along with an expanding ecosystem that delivers a broad range of services. MapR Technologies' CEO and co-founder John Schroeder provided the information for this eWEEK slide show, which identifies 10 major developments that will continue to drive big data adoption as well as Hadoop's role in this trend.

 
 
 

Hadoop Will Be Used More in Real-Time Applications

Hadoop's capabilities now make it possible to stream data into the cluster and analyze it in an interactive fashion—both in real time. Hadoop was purpose-built for cost-effective analysis of data sets as enormous as the World Wide Web.

Hadoop Will Be Used More in Real-Time Applications
 
 
 
 
 
 
 
 
 
 
 

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