Zementis and Teradata Announce In-database Scoring for Big Data

As a result of its partnership with Teradata, Zementis is excited to announce the availability of the Universal PMML Plug-in (UPPI) for Teradata analytic platforms. It does not get easier than this! Simply deploy your predictive models built in R, IBM SPSS, SAS EM, ... and score your big data, directly in-database, where it resides.

The Zementis Universal PMML Plug-in (UPPI) enables the execution of standards-based predictive analytics directly within the Teradata Unified Data Architecture™. Users can now easily deploy predictive models built in R, IBM SPSS, SAS EM and other popular analytic tools on Aster and/or Teradata to achieve scale. The bridge between these systems is PMML, the Predictive Model Markup Language standard. It allows for models to be instantly moved from the scientist's desktop to the database where they will be executed.

As described by Teradata's Chris Twogood, VP for Product and Services Marketing, "by partnering with Zementis, we are able to offer high performance, enterprise-level predictive analytics scoring for the major analytics tools that support PMML. With Zementis and PMML, we are eliminating the need for customers to recode predictive analytic models in order to deploy them within our database. In turn, this enables an analyst to reduce the time to insight required in most businesses today." Available for Teradata and Teradata Aster databases, UPPI leverages the massively parallel databases as a scalable, high-performance, scoring engine that easily processes through petabyte-scale data volumes. UPPI takes full advantage of the high-performance data warehouse with its massively parallel processing capabilities for rapid execution of standards-based predictive analytics based on the PMML standard. Models built in most commercial and open source data mining tools can now instantly be deployed in Teradata or Aster. The net result is the ability to leverage the power of standards-based predictive analytics on a massive scale, right where the data resides. Read the full press release!