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IBM Releases Big Solutions for Big Data

As organizations grapple with a flood of structured and unstructured data – Big Data – generated by computers, mobile devices, sensors and social networks, they’re under unprecedented pressure to analyze much more data at faster speeds and at lower costs to help deepen customer relationships, prevent threat and fraud, and identify new revenue opportunities.

So, tech company IBM (NYSE: IBM) announced Wednesday new technologies designed to help companies and governments tackle Big Data by making it simpler, faster and more economical to analyze massive amounts of data. New data acceleration innovation results in as much as 25 times faster reporting and analytics, claims IBM.

IBM announcement includes “BLU Acceleration,” which combines a number of techniques to improve analytical performance and simplify administration. Also announced is the new IBM PureData System for Hadoop, designed to make it easier and faster to deploy Hadoop in the enterprise.

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Hadoop is open-source software used to organize and analyze vast amounts of structured and unstructured data, such as posts to social media sites, digital pictures and videos, online transaction records, and cell phone location data.

IBM says the new system can reduce from weeks to minutes the ramp-up time organizations need to adopt enterprise-class Hadoop technology with powerful, easy-to-use analytic tools and visualization for both business analysts and data scientists. In addition, it provides enhanced Big Data tools for monitoring, development and integration with many more enterprise systems.

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“Big data is about using all data in context at the point of impact,” said Bob Picciano, general manager, IBM Information Management. “With the innovations we are delivering, now every organization can realize value quickly by leveraging existing skills as well as adopt new capabilities for speed and exploration to improve business outcomes.”

The IBM Big Data platform combines traditional data warehouse technologies with new Big Data techniques, such as Hadoop, stream computing, data exploration, analytics and enterprise integration, to create an integrated solution to address critical needs.

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