Predictive Data Analytics
UPPI for SAP Sybase IQ
Universal PMML scoring for SAP Sybase IQ
Zementis® and Sybase® have teamed to deliver a comprehensive solution to help companies to easily deploy, execute and integrate scalable standards-based predictive analytics. It combines the Zementis Universal PMML Plug-in (UPPI) for real-time execution of models with the power and scale of SAP Sybase IQ, a highly optimized analytics server designed specifically to deliver superior performance for mission-critical business intelligence, analytics and data warehousing solutions on any standard hardware and operating system.
With Sybase IQ, UPPI offers Sybase users the best combination of open standards and scalability for the application of predictive data analytics.
UPPI for Sybase IQ delivers instant and scalable scoring for big data while retaining compatibility with most major data mining tools through the PMML Standard. Through its versatile use of two essential technologies, the Zementis/Sybase partnership:
- Brings the scalability of Sybase IQ to the execution of predictive data analytics
- Supports PMML to avoid time-consuming and expensive one-off predictive data analytics projects
- Provides cost effective storage and processing of large volumes of highly granular data that predictive applications often require
- Brings together a 100% standards-based approach to analytics that lowers total cost of ownership and increases reuse control and flexibility for orchestrating critical day-to-day business decisions.
For more details about UPPI for Sybase IQ, feel free to: 1) contact us; 2) watch a Zementis/Sybase webinar which contains an introduction and demo to our products; 3) access the SAP Sybase IQ Marketplace; 4) download our joint WHITE PAPER; 5) download the SAP – Big Data Analytics Guide (look for our article in page 48); or 6) download the UPPI for Sybase IQ Product Data Sheet.
Also, make sure to take a look at the SAP Sybase IQ white paper about PMML entitled Predictive Model Markup Language (PMML) – Accelerating the Time to Value for Predictive Analytics in the Big Data Era.