I must say, I am always impressed by the IBM universe of products and tools that are being offered for analytics (descriptive and predictive) as well as Big Data in general. Zementis had a booth inside the Pure Data exhibit area and next to all the Pure Data appliances. As you can imagine, traffic was solid not just because of all the blinking lights but also because the conference itself attracts a lot of people. I believe there were 14 thousand attendants this year.
Why in-database scoring? Well, simple. Not all analytic tasks are born the same. If one is confronted with massive volumes of data that need to be scored on a regular basis, in-database scoring sounds like the logical thing to do. In all likelihood, the data in this case is already stored in a database and, with in-database scoring, there is no data movement. Data and models reside together hence scores and predictions flow on an accelerated pace.
Why scoring in Hadoop? 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. 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.
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.