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Large amount of data that is characterized by its volume, velocity, veracity, value and variety is termed Big Data. Extracting hidden patterns, customer preferences, market trends, unknown correlations, or any other useful business information from large collection of structured or unstructured data set is called Big Data analysis. This article exp lores the scope of analyzing bank transaction data to categorize customers which could help the bank in efficient marketing, improved customer service, better operational efficiency, increased profit and many other hidden benefits. Instead of relying on a single technology to process large scale data, we make use of a combination of strategies like Hadoop, PIG, R etc for efficient analysis. RHadoop is an upcoming research trend for Big Data analysis, as R is a very efficient and easy to code, data analysis and visualizat ion tool compared to tradit ional MapReduce program. KMeans is chosen as the clustering algorithm for classification.

A Novel Big Data Approach to Classify Bank Customers - Solution by Combining PIG, R and Hadoop