Teradata Unifies SQL and Hadoop

Michael Vizard

Ever since the first Hadoop system found its way into the enterprise, IT organizations have been looking for ways to couple SQL to Big Data. While there are already a number of options available to do just that, Teradata is taking that need for integration to a whole other level.

The company today unveiled the Teradata Aster Big Analytics Appliance, which combines Hadoop with an instance of the massively parallel database technology that Teradata gained with the acquisition of Aster Data last year.

According to Tasso Argyros, co-president of Teradata Aster, rather than simply hosting the two databases on the same platform, Teradata has integrated them at a deeper metadata level. Using a distribution of Hadoop from Hortonworks, Teradata is providing support for both MapReduce and a fully ANSI-compatible instance of SQL known as SQL-H to make it easier for applications to query structured, semi-structured and unstructured data. In addition, the appliance comes with a library of over 50 pre-built functions for running analytics to help organizations derive immediate value from their investment, says Argyros.

Using a 40 Gigabit-per-second InfiniBand connection, the Teradata Aster Big Analytics Appliance can be integrated with an existing data warehouse from Teradata to create a data management platform that spans Hadoop, a massively parallel database and a traditional SQL database system.

Teradata also released AsterExpress, a downloadable version of the new offering that is intended to make it easier for developers and data scientists to get a jumpstart on building applications on top of a virtual appliance.

As powerful as Hadoop is there are still many issues to be addressed, not the least of which is the performance of a system that is essentially based on “micro-batch” architecture. At the same time, IT organizations have billions of dollars invested in SQL. Rather than wasting time arguing over which approach is better, the reality is that each approach lends itself to different use cases. The challenge is finding ways to unify those platforms in a way that not only creates new unique applications that add a lot of business value, but also keep the costs associated with having multiple data management platforms to a minimum.

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