When the demands of Big Data analytics surpass the core count and memory available on your biggest server, you’re usually left with three dismal options: spend money you don’t have on new hardware; devote time you can’t spare rewriting code to run across clusters; or delay insights you can’t put off by shrinking the size of your problems to fit the limits of your hardware.
For users relying on R, TERR, Spark, Python and other tools that benefit from large systems, there’s a fourth option – Software-Defined Servers. Software-Defined Servers enable data scientists to scale their computer, including cores and memory, to fit the size of their problem – all on demand, and without changing a single line of code or adding a single piece of new hardware. For those working with R, it means you can scale without unnecessarily complicating your code.
Now, thanks to a new complimentary webinar, you’ll have an opportunity to understand how it all works. In “How to Keep Your R Code Simple While Tackling Big Datasets,” TidalScale’s Michael Berman will demonstrate how Software-Defined Servers work in practice for several common data science tools. He’ll also explore how removing core count and in-memory constraints have profound and positive implications for application developers tackling Big Data problems of all kinds.
Carve out an hour at 9 a.m. PST Tuesday, Feb. 14 for this complimentary one-hour webinar hosted by Bill Vorhies, Editorial Director at Data Science Central.
Aren’t you ready for a breakthrough of your own? Watch our Data Science Central Webinar and discover how Software-Defined Servers keep things simple when the demands of Big Data are anything but.