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Teradata Aster Connector for Spark Delivers pre-built analytics

Teradata Aster Connector for Spark Delivers pre-built analytics

Teradata Aster Connector for Spark, an industry-first integration of Apache Spark analytics with Teradata Aster Analytics. The connector enables pre-built analytics functions from both solutions to be executed from Aster Analytics to form a truly multi-genre advanced analytics environment. The result is that virtually anyone who can use Aster Analytics can also run advanced analytics on Spark without the need to learn or know Scala.

The Teradata Aster Connector for Spark democratizes big data through self-service, business- focused, analytic solutions. By enabling ease-of-use for many business users, companies can more quickly identify revenue-driving insights and accelerate business performance. Specifically, the Teradata Aster Connector for Spark gives users many choices and benefits:

1) Customers can now use techniques from both Aster Analytics and Spark (example, Teradata Aster nPath, used for pattern matching, and deep learning neural network analysis with Spark), and can choose the technique for implementation that generates the best insights upon evaluation.

2) Customers can pipeline various functions together in one workflow that can be executed in Aster Analytics. For example, a text parser function from Aster Analytics can be invoked, followed by a Spark machine learning algorithm, to support the development of an illuminating data model. This sequence can be replicated for other function types.

3) Customers can run a clustering algorithm in Aster Analytics, and a similar one in Spark, and compare results to see which approach is preferred.

“There’s huge interest in the in-memory performance and analytical capabilities of Spark, but the universe of data professionals with Spark skills and experience is still quite small as compared to those with SQL skills,” said Doug Henschen, vice president and principal analyst, Constellation Research. “Customers seek an ensemble of analytical capabilities expressed in SQL and SQL-like expressions. Moreover, they want in-memory performance and analytical capabilities while abstracting users from complex and unfamiliar Spark interfaces and coding.”

Raghu Chakravarthi, vice president of Teradata Aster Engineering, emphasized the value of use cases enabled by the Teradata Aster Connector for Spark.

“The beauty of the Teradata Aster Connector for Spark is its application for a variety of use cases in just about any industry,” said Chakravarthi. “For instance, Aster can be the repository for customer data and finance data. Once Aster pre-processes these, machine learning from Spark can be applied to create automatic credit ratings for each customer. Analysts could then use these credit ratings as one variable in a predictive model that ascertains the likelihood of, say, this customer purchasing a new automobile in the next 12 months.”

Another use case arena for the Teradata Aster Connector for Spark is The Internet of Things, where large volumes of sensor data are ingested and pre-processed using Aster Analytics. This data can be passed on to Spark for analysis using deep learning techniques. Data on home sensors and thermostats can be combined with other information such as geo-location, resident demographics, and weather conditions to determine usage patterns, to predict instrument wear and tear, and to proactively activate household appliances in response to changing environmental conditions.

Chakravarthi also noted examples in the retail sector, where customer transaction information could be passed to a clustering algorithm like k-means to create product groups – and combined with customer comment data or product reviews to create new insights. He also said financial institutions could mine interaction information to understand the distinct set of conditions that could result in churn. Using Spark to ingest parsed and transformed data from Aster Analytics, additional analytics could be run to determine churn drivers.

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