With big data quickly picking up steam, companies are scrambling to launch their own data analytics programs. Since the very beginning, Hadoop has served as the platform of choice for data scientists looking to design their own analytics programs. Unlike proprietary alternatives, the open source platform provides an inexpensive and comprehensive resource for high-powered data processing. Recent studies have shown that, although more companies than ever before are turning toward Hadoop file systems for their big data needs, many organizations have struggled to get their programs off the ground.
According to a survey conducted by Dimensional Research, most companies' Hadoop big data projects have yet to be launched. The study found that 24 percent of data management professionals reported having a Hadoop project in full production. Half have yet to move past the planning stage. Possible reasons for these figures include the technology's relative infancy and the dearth of data scientists needed to derive actionable insights.
The amount of information being gathered and analyzed continues to grow, as 19 percent said they were managing more than 500 terabytes of data. The study also found that one of the major bonuses of a Hadoop architecture was its cost-effectiveness, as the platform allows users to scale vertically without a significant budgetary expense.
Chris Preimesberger of eWeek reasoned that companies are flocking to Hadoop because of its wide range of applications and usability. While other platforms have been designed to fit narrow needs, Hadoop offers a variety of resources that can be employed by any user. Furthermore, the platform's increasing ability to process data in real time will provide analysts with even more functionality moving forward. As Hadoop architecture becomes the standard for big data solutions, analysts can expect data centers to increasingly adopt server equipment specifically designed to harness the power of the platform.