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Storing, analyzing and accessing data is a growing problem for organizations. Competitive pressures and new regulations are requiring organizations to efficiently handle increasing volumes and varieties of data, but this doesn't come cheap. And as the demands of Big Data exceed the constraints of traditional relational databases, evaluating legacy infrastructure and assessing new technology has become a necessity for most organizations, not only to gain competitive advantage, but also for compliance purposes. The challenge is how well the organization's legacy infrastructure integrates Big Data. It is without a doubt that one way or another Big Data must be accommodated by legacy systems. Legacy systems contain significant and invaluable business logic of the organization. Organizations cannot afford to throw away or replace this business logic. These legacy systems are assets of the organization. These invaluable assets of encoded "business logic" represent many years of coding, development, real-life experiences, enhancements, modifications, debugging etc. Most of the legacy systems were developed without the process models or data models-now needed to support and integrate Big Data. To integrate Big Data into legacy system, modernization of legacy system is required. There are many approaches for modernization of legacy systems but none of them are focused on integrating Big Data into legacy systems. Legacy systems hold valuable data too important to be lost in the process of modernization. However, addressing the issues and scope related to incorporating Big Data with legacy systems allows mature legacy systems to become part of groundswell changes. There are many areas unaddressed about integration of Big Data into legacy systems. Incorporating data from new sources, specifically "live" sources, into existing legacy systems is a technical challenge. Moreover, the sheer volume of Big Data can be daunting. Our paper presents the scope of integrating Big Data into modernization of legacy systems.

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