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“EXCELLENCE THROUGH KNOWLEDGE” P A G E 82 Increasing the Accessibility to Big Data Systems Via a Common Services API Despite the plethora of polls, surveys, and reports stating that most companies are embracing Big Data, there is slow adoption of Big Data technologies, like Hadoop, in enterprises. One of the primary reasons for this is that companies have significant investments in legacy languages and systems and the process of migrating to newer (Big Data) technologies would represent a substantial commitment of time and money, while threatening their short-term service quality and revenue goals. In this paper, we propose a possible solution that enables existing infrastructure to access Big Data systems via a services application programming interface (API); minimizing the migration drag and (possibly negative) business repercussions. Amazon, Apple, Facebook, Twitter, Netflix, and Google have set the standard for the current and emerging era in computing. Their core business is built on collecting, analyzing and monetizing large quantities of data. The magnitude of data and the processing required (within user expectations of response time) precludes the use of traditional data management technologies and has ushered in the age of Big Data. Midsized and large organizations recognize the benefits of investing in Big Data systems; but have been slow in their adoption due to varying reasons. They range from lack of necessary skills as data scientist to the financial uncertainty on how one qualifies the tradeoffs on the return on investment (ROI) in the short run as opposed to the log run outlook of a business. Other concerns surround effort, value decay, service degradation and disruption from porting current systems to newer infrastructures and technologies. We purport that the latter rationale can be partially mitigated by technology. In this paper, we propose a mechanism for methodically converting the current legacy enterprise application stack to one that leverages the latest and greatest Big Data technologies; while lessening the effort required and the possible disruption to the firm’s value proposition and quality of service agreements. We begin by presenting the fundamentals: What is Big Data? (Section II); and what is a typical Big Data stack and how it works (section III). In Rohan Malcolm1, Cherrelle Morrison1, Tyrone Grandison2, Sean Thorpe1, Kimron Christie1, Akim Wallace1, Damian Green1, Julian Jarrett1, Arnett Campbell1 1University of Technology, Jamaica 2Proficiency Labs International, USA Sean Thorpe