In today's business context it is found that conventional databases are not sufficient in dealing with the data being generated for mostly two reasons one is the volume and the second is the multiple sources as well as its heterogeneous nature. Volume is a concern as size of data grows it implies more time to process it and it is more expensive to manage as well in traditional databases. Similarly the new forms of unstructured data, a source of big data, which enterprises are finding to be

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... l, cannot be stored or processed using existing models of RDBMs. A vast volume of data generated may not naturally fit into traditional storage systems because it is unstructured and will not fit into the conventional storage systems. Largely enterprises are realizing the importance of advances in the area of Big data. Some of the issues enterprises are facing are how to analyze these and what mathematical models to apply. Here is the where semantic web technologies coupled with Big data techniques can give powerful solutions to these problems. Big data systems do not exist in isolation and there will be need to find a path to link them with the conventional systems. There is need for mechanisms that allow seamless information flow between Big data systems and conventional systems. Semantic Web Technologies have matured and may prove essential in representing the unstructured data in a form where Big data processing can be applied.This paper is an attempt to discuss the big data challenge and opportunity, unstructured data and semantic technologies. The aim is to explore these in the context of enterprise applications and how enterprises may gain from these technologies.