We present the details of a large scale user profiling framework that we developed here on Apache Hadoop. We address the problem of extracting and maintaining a very large number of user profiles extracted from large scale data. In this work, a user profiles is often used to classify a given user into pre-defined user segments or to capture the online behavior of the user including the user’s private interests and preferences. A user profiles can be explicitly defined by the user himself. User Profiling is usually defined as the process of implicitly learning a user profiles from data associated with the user. The Data extracted in stored form of the xlsx, pdf, docx format in certain Data-marts or organization is also extracted to get user information and personalize the user’s behavior accordingly. Data sources for user profiling include among others the user’s browsing sessions or even other user profiles using collaborative filtering techniques.