Oct. 8, 2013
/PRNewswire/ -- Teradata (NYSE: TDC), the leading analytic data platforms, applications and services company, today introduced the Teradata® Aster® Discovery Platform, which is part of a new generation of solutions empowering chief information officers to execute a cohesive big data strategy creating an unfair competitive advantage.
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Data scientists and analysts have been plagued by a piecemeal big data strategy, rudimentary tools, lack of scalability, and the use of desperate tools, technologies, and processes. "Recent IDC research revealed that only 10 percent of organizations have the features and functionality needed to explore data and discover insights. There are clearly many opportunities for improvement," said Dan Vesset, program vice president, Business Analytics and Big Data, IDC. "An integrated data discovery platform should be part of every organization's portfolio. Without this capability, organizations have a real void in their business analytics strategy."
"The innovative design of the Aster Discovery Platform will liberate data scientists around the world by reducing complexity, breaking down analytic silos, and magnifying analytic ability," said
, president, Teradata Labs. "The Aster Discovery Platform is the first to offer cross-analytic engine optimization and data processing innovations that make it faster and easier for a wider group of users to leverage Aster's analytics. The Aster Discovery Platform will fuel innovation and profoundly impact business results."
The innovations in the Aster Discovery Platform include Teradata Aster SQL-GR™, a graph engine, and the Teradata SNAP Framework™ (Teradata Aster Seamless Network Analytic Processing Framework™).
New Graph Analytic Engine
Users may customize their Aster analytics with Teradata Aster SQL, Teradata Aster SQL-MapReduce®, or the newly developed Teradata Aster SQL-GR™ engine. These engines empower organizations to analyze data in new and interesting ways to uncover never seen before business insights.
The Aster SQL-GR graph engine is unique because it is scalable, processes big data in parallel, and is not limited by system memory. Its analytic capability can extend to millions of nodes or processing units. The Aster SQL-GR engine enables native processing of large-scale analytic graph queries and pre-built graph functions and can be used for customer churn, product affinity, fraud detection, and recommendation engines. For example, telecommunications providers can use graph analysis to look for high-traffic connections among selected users, which provides clues to fraudulent calling activity.