Teradata recently announced a new version of its Aster Discovery Platform, which contains important enhancements to the existing functionality set, aiming to deal with the analysis of big data.

The Teradata Aster Discovery Platform addresses the issues associated with an organization’s big data analytics requirements and aims to reduce the difficulties associated with deploying and managing big data analytics initiatives (from data collection to visualization and consumption).

Some of the new capabilities provided in this release include:

• A new set of Visual SQL-MapReduce functions to offer capabilities for generating sophisticated purpose-built data visualizations coupled with other Aster SQL-MapReduce analytical functions generated inside the database

• In-Database “R” execution capabilities—the open source statistical language, R, is integrated within the Teradata Aster Database.

• Support for In-Database PMML execution via Zementis to tight integration between Zementis and Teradata Aster Database, empowering business analysts to develop statistical models in the tools of their choice.

• In-Database Integration of Attensity’s capabilities—text and sentiment analytic capabilities from Attensity are being integrated with the Aster Database.

• Integration of manufacturing and financial services industry analytics to assist manufacturing and financial services companies, along with new SQL-MapReduce functions integrated

Scott Gnau, president of Teradata Labs, had this to say:

Existing and aspiring data scientists should take note. The Teradata Aster Discovery Platform is full of new capabilities that can empower them to accelerate their innovation and supply new options to their business users. It is Teradata’s goal to be our customers’ trusted partner in building a Unified Data Architecture, which includes a powerful set of big data solutions. The result is effective deployment of transformational technology that drives tangible results.

Aster has a very unique and interesting model to approach big data from combining the abilities of traditional SQL and MapReduce mechanisms to provide an easier yet disciplined approach to working with both structured and nonstructured data.

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