Launching HFlame - Transform Hadoop into real time big data analysis platform

Oct. 19, 2012 - PRLog -- Data Advent Inc. (a big data technology start up based in India) is announcing the release of beta version of HFlame - Real Time Hadoop.

This release of HFlame addresses one of the most visible Achilles' heels of Hadoop - Hadoop's non real time data analysis. While Hadoop is becoming preferred data analytics platform for many big data problems, its non real time nature makes it practically useless for business problems that need immediate insights into new data. This serious limitation of Hadoop is resulting into proliferation of many alternative big data management technologies/framework, which is further convoluting the big data management technology space.

HFlame augments standard Hadoop distributions with real time data analysis capabilities. With HFlame, Hadoop platforms are capable of running continuous analysis on data streams stored inside Hadoop file system (HDFS). HFlame uniquely empowers enterprise to run real time big data analytics without introducing any additional technology component (e.g. data storage or data processing APIs). With HFlame, same Hadoop platform can meet both offline and online data analytics needs of enterprise and deliver instant insights without needing any alternate technologies.

Additionally HFlame tremendously benefits traditional offline Hadoop analytics by speeding up execution and needs significantly smaller sized cluster. A traditional Hadoop job is capable of producing up to 100x faster results and drives down the resource requirement up to 10x.

HFlame comes in two flavors - Community Edition and Enterprise Edition. Community Edition is free of charge full-featured version of HFlame, with limits on number of concurrent real time Map-Reduce job and number of nodes in Hadoop cluster. Enterprise Edition relaxes constraints of Community Edition and additionally entitles the users for technical support and indemnifications.

Web - http://www.hflame.com
Facebook - http://www.facebook.com/hadoop.flame
Twitter - @hadoopflame