Big Data and Hadoop are somewhat synonymous terms these days, since the latter offers an important technological platform to tackle the challenge of analyzing large volumes of data. In fact, predictive analytics is paramount for companies to extract value and insight from such data. It is in this context that Zementis brings its standards-based predictive scoring engine into a variety of Big Data platforms, including the cloud as well as in-database. By offering the Universal PMML Plug-in (UPPI) for Hadoop, Zementis takes a big step in making its technology available for companies around the globe to easily deploy, execute, and integrate scalable standards-based predictive analytics on a massive parallel scale through the use of Hive, a data warehouse system for Hadoop, and Datameer, an end-to-end BI solution that works on top of Hadoop.
UPPI brings together essential technologies, offering the best combination of open standards and scalability for the application of predictive analytics. It fully supports the Predictive Model Markup Language (PMML), the de facto standard for data mining applications, which enables the integration of predictive models from IBM/SPSS, SAS, R, and many more.
Hive makes it possible for large datasets stored in Hadoop compatible systems to be easily analyzed. Since it provides a mechanism to project structure onto the data, Hive allows for queries to be made using a SQL-like language called HiveQL.
Once deployed in UPPI, predictive models turn into SQL Functions. These can then be invoked directly in HiveQL. In this way, UPPI offers Hadoop users the best combination of open standards and scalability for the application of predictive analytics.
UPPI for Hadoop/Hive delivers instant and scalable scoring for Big Data while retaining compatibility with most major data mining tools through the PMML Standard. It also brings brings the scalability of Hadoop to the execution of predictive analytics.