For machine learning, Hadoop offers new performance capabilities, but not the intrusion of Hadoop's accompanying tradeoffs--performance, resource consumption, and data management. Machine learning users should consider Hadoop as a portion of a solution, but not the end-all. Alternatives such as dedicated servers, in-database deployment, and memory-based alternatives like Apache Spark can be combined with Hadoop to address a far broader array of opportunities. Fortunately for Revolution R users, Revolution R Enterprise (RRE) enables analytical scripts and models built in RRE to port between platforms with relative ease. In this session, we'll review the considerations for R developers, including performance, resource management, and data handling for deployment on Hadoop, individual servers, clusters and grids, in-database, and in-memory, including Apache Spark. We'll also dive briefly into the internals RRE on Hadoop to deepen awareness of some of the tradeoffs.
Design guru Tommy Lewis wants your apps to look their best. He emphasizes that attractive apps stand out from the rest, so a focus on design is a must. He describes a "visual" way of thinking, covering what to expect during the design phases of your development and what goals to work towards. Last but not least, he covers design principles that you should think about as you design your apps: authentically digital, more with less, pride in craftsmanship, fast and fluid, and win as one.
Even though you're watching this on-demand, Tommy wants to help. Post your questions, comments, etc below or tweet it, mentioning @cdndevs.