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 explores 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 visualization tool compared to traditional MapReduce program. K-Means is chosen as the clustering algorithm for classification.

The amount of data that is generated each day is rapidly increasing so that the traditional ways of data storage seems to be inadequate. Cloud Computing has helped a lot to solve the storage issues but fear of data leakage hinders its widespread acceptance. Even though the public clouds like Amazon, Google etc store the data in encrypted form, data loss can still occur during transmission. The only practical solution is to upload encrypted data to cloud. Hence users should have an additional software or application to encrypt the data before uploading and decrypt the data after retrieval. This article analyzes different solutions for implementing such applications. The article also describes about the cryptographic primitives that can be added for enforcing security.