The data world was rocked earlier this week by the Google I/O conference and the announcement about Google Cloud Dataflow, which essentially leaves Map/Reduce by the wayside in favor of newer technologies more adaptable to the exponentially-increasing amount of data. But announcements are just that, and life goes on; Teradata announced Teradata Aster R this week, which seeks to lift memory and processing limitations in open source R analytics.
R analysts are challenged as they try to gain the maximum benefit from R when it is deployed on a single server and only runs in-memory. The single server, in-memory environment deployment restricts the amount of data that can be processed in-memory and can lead to slow performance of complex analytics. Teradata lifts the processing and memory limitations by offering parallel, in-database execution for R analytics. Executing R in-database allows for high-speed processing of massive quantities of data to meet the analytic needs of the organization. In addition, Teradata enables R analysts to access and integrate detailed data from multiple sources, and deploy a wider range of analytics for enhanced results.
While waiting for the inevitable shift in how things are done, data will keep streaming in; 70 percent of data miners who responded to a survey say they use open source R. Check the news out over at insideBigData.