Teradata introduces Aster Big Analytics Appliance

Infotech Lead India: Teradata has introduced the Teradata Aster Big Analytics Appliance.

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Teradata claims that this appliance offers up to 19 times better data throughput and performs analytics up to 35 times faster than a typical off-the-shelf commodity bundle.

Moreover, Aster Big Analytics Appliance has ample memory and high bandwidth interconnects to support extreme analytic execution, and it is packaged to require less space in the data center than off-the-shelf offerings.

The Teradata Aster Big Analytics Appliance brings together open source Apache Hadoop and Teradata Aster into a single optimized appliance.

The Teradata Aster Big Analytics Appliance provides transparent access to Hadoop and provides business analytics to knowledge workers. The appliance offers pre-packaged ready-to-run analytical functions such as digital marketing optimization, social network analysis, fraud detection, and analysis of machine-generated data in just hours.

The enterprise-ready Teradata Aster Big Analytics Appliance is designed to break down the barriers that traditionally slow down adoption of big data analytics. Teradata Aster Big Analytics Appliance brings the most sophisticated packaging in the market. With flexible configuration options, companies can configure Aster Database and open source Hadoop all in a single integration cabinet.

“The Teradata Aster Big Analytics Appliance offers the faster path from diverse big data acquisition to big insights, and seamlessly delivers these insights to the business owners,” said Scott Gnau, president, Teradata Labs. “Unmatched by any other stack in the industry, it enables organizations to overcome the barriers to big data analytics and provides a high-definition view of the business to optimize operations.”

Industries such as retail, ecommerce, consumer packaged goods, communications and financial services needing deeper insights into consumer behavior can use the new Teradata appliance capabilities to harness multi-structured data from social media, Web clickstream, Twitter streams, call center activities, and other types of customer interaction data.

“When we add big analytics to big data and begin to use information that’s in other formats other than relational database rows – like Web logs, content management systems and clouds of instruments on the Internet, we wind up with data types that are compressed into formats that first need to be teased apart for analysis. Gartner clients tell us that combining scored, processed ‘outside data’ with data inside our relational databases is where all the added value is,” said Merv Adrian, research vice president, Gartner.

 “As Apache Hadoop moves from early adopters to the main stream, organizations require enterprise-class, unified system management and the ability to tightly integrate with their existing analytical tools to ensure success,” said Rob Bearden, chief executive officer, Hortonworks.

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