DataWeek Conference + Expo

Quick and Dirty: Scaling Out Predictive Models Using Revolution Analytics on Teradata

I recently faced the task of how to scale out an existing analytics process. The schedule was compressed - it always is in my world. The data was big - 400+ million rows waiting in database. What did I do? I offered my favorite type of solution - quick and dirty.

At the outset, I wasn't sure how easy it would be. Nor was I certain of realized performance gains. But the concept seemed sound and the exercise fun. Let's move the compute to the data via Revolution R Enterprise for Teradata.

This presentation outlines my approach in leveraging a colleague's R models as I experimented with running R in-database. Would my path lead to significant improvement? Could it be used to productionalize the workflow?

Applications of R: How companies use data science to succeed

Adoption of the R language has grown rapidly in the last few years, and is ranked as the number-one data science language in several surveys. This accelerating R adoption curve has been driven by the Big Data revolution, and the fact that so many data scientists — having learned R at university — are actively unlocking the secrets hidden in these new, vast data troves.     In more than 6 years of writing for the Revolutions blog, I’ve discovered hundreds of applications of R in business, in government, and in the non-profit sector. Sometimes the use of R is obvious, and sometimes it takes a little bit of detective work to learn how R is operating behind the scenes. In this talk, I'll recount some of my favourite applications of R, and show how R is behind some amazing innovations in today’s world.