Establishing a tight integration between R and a massively parallel processing (MPP) analytic database requires integrating a variety approaches and the simplifying contract of virtual objects. Virtual objects allow the user to manage and navigate data on distant data sources as if they were normal R objects and to execute big data analytic processes without skills or coding structures that seem arcane to normal R users. Among the integration approaches that are required are processes that simplify the passage of data between the local environment and the analytic database's extended environment. Automatic data type matching and bulk data movement processes must be combined with powerful data transformation, pattern extraction, and aggregation processes.
Join two early adopters from Wells Fargo and Teradata as they explore how to use R with enterprise level data, parallel processing, and multi-genre analytics
