The growth in the capacity and capability of NAND Flash based storage systems have changed the face of data oriented computational systems. These systems have become both more capable and flexible in how they are used. With these changes comes both increased potential and user complexity. While many systems attempt to hide this complexity through the addition of more layers of storage caches, the design of the Wrangler system went a different route, choosing instead to build a simple yet flexible web based interface to allow users to easily configure this complex data computing system based on their service and software needs. This allows users to work in the environments best suited to their workflows while optimally utilizing the systems high performance and high capacity storage systems. This interface also allows users to schedule long term periods of reserved capacity, "data campaigns", for projects. Finally, the system has been designed to support the data storage and sharing capacities of the system to enable these key aspects of data research. We discuss the capabilities with respect to three already existing workflows on the system to highlight the diversity and flexibility provided by this environment to data researchers.

Over the years, R has been adopted as a major data analysis and mining tool in many domain fields. As Big Data overwhelms those fields, the computational needs and workload of existing R solutions increases significantly. With recent hardware and software developments, it is possible to enable massive parallelism with existing R solutions with little to no modification. In this paper, we evaluated approaches to speed up R computations with the utilization of the Intel Math Kernel Library and automatic offloading to Intel Xeon Phi SE10P Co-processor. The testing workload includes a popular R benchmark and a practical application in health informatics. There are up to five times speedup gains from using MKL with a 16 cores without modification to the existing code for certain computing tasks. Offloading to Phi co-processor further improves the performance. The performance gains through parallelization increases as the data size increases, a promising result for adopting R for big data problem in the future.