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IBM Netezza High Capacity Appliance Addresses Big Data Challenge


IBM has introduced a new analytics appliance that is intended to allow organizations to analyze up to 10 petabytes of data in minutes, helping them uncover patterns and trends from large data sets, while meeting compliance mandates. The new IBM Netezza High Capacity Appliance addresses a growing challenge: Banks, insurance companies, healthcare organizations and communications services providers are required by industry regulators to retain massive amounts of data – in some cases up to a decade. And, as data retention laws continue to evolve, organizations are faced with the need  to store and analyze ever-expanding “big data” sets that may not be directly related to daily operations, yet still hold potential business value.

IBM recently announced a $100 million investment for continued research on technologies and services that will enable clients to manage and capitalize on data as it continues to grow in diversity, speed and volume.

Using the new appliance, IBM says, businesses can now more easily sift through petabytes of data including banking and mobile phone transactions, insurance claims, electronic medical records and sales information. Companies can also analyze this information to reveal new trends on consumer sentiment, product safety, and sales and marketing effectiveness. 

IBM Netezza appliances are workload-optimized systems based on IBM BladeCenter technology. The appliance is the first to be delivered by IBM since it acquired Netezza in November 2010.

IBM Netezza technology joins IBM’s Hadoop-based BigInsights software, as well as Streams software -- both born in IBM Research - creating a broad portfolio of workload optimized big data technologies. The software incorporates Watson-like technologies, including unstructured text analytics and indexing that allows users to analyze rapidly changing data formats and types on the fly.  

For information about IBM Netezza, please visit: www.netezza.com.

For Information on IBM business analytics, visit www.ibm.com/analytics.


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