Teradata Announces Aster Analytics Updates

Teradata today announced updates to its Aster Analytics solution that introduce new deployment options for the advanced analytics engine: Teradata Aster Analytics on Hadoop and Teradata Aster Analytics on Amazon Web Services (AWS). The new options are part of a broader initiative by Terada to develop a Hybrid Cloud architecture that can support large volumes of data from the Internet of Things (IoT).

"Teradata acquired Aster several years ago for its analytics capabilities," says Arlene Zaima, senior global product marketing manager at Teradata Aster. "What it allows us to do is complement the existing analytics capabilities…[It] adds contexts to those analytics by identifying influencers—looking at a network of customers and finding out who influences the other customers, or a path to a purchase, because there may be a lot of different advertisements or promotions that influenced a customer."

Designed to expand the use and value of the Hadoop data lake, Aster Analytics on Hadoop makes Hadoop accessible to business analysts with SQL and R skills. The analytics platform runs natively in Hadoop, cutting down on costs, delays, and security risks by ensuring that users don't need to move their data into an analytic server. Aster Analytics on Hadoop also features an AppCenter for users to build web-based interfaces.

"The reason why our customers asked us to move [Aster Analytics] to this environment was not just so they can analyze the data in that environment, because they could easily pull the data out of Hadoop and run it on a server somewhere," Zaima says. "Our customers understand that if you run the analytics directly on that system that houses the data, it's so much more efficient, because you eliminate the need to move data back and forth. Moving the data takes up quite a bit of your time; that's why we've [enabled] Aster to run natively in Hadoop."

Aster Analytics on AWS is designed to help firms in three key areas. First, it speeds time to value by enabling businesses to quickly provision an analytic sandbox in the cloud and use Aster's prebuilt SQL-based analytics to accelerate development. Second, it boosts analytic agility by providing analysts with multigenre analytics at scale, which they can use to experiment with and iterate large volumes of data. Third, it allows organizations to experiment with prebuilt advanced analytics options and their data assets without paying for new hardware, setup, or implementation.

"This is the first time we're going to release our software in Amazon marketplace—this is fairly new to us, and we're very excited," Zaima says. "We call it multigenre advanced analytics because we don't just provide machine learning, we don't just provide graph analytics to find influencers, it's just not time series or statistics—we combine all these techniques together, all within a single solution." 

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