In recent years the amount of data generated in educational sector is growing rapidly. In order to gain deeper insights from the available data and extract useful knowledge to support decision making and improve the education service efficient storage management and fast processing analytics is needed. Academic data of a student helps institute to measure their progress. Students facing severe academic challenges are often recognized too late. Analytics play a critical role in performing a thorough analysis of student and learning data to make an informed decision. Big Data solution enables to analyze the wider variety of data sources and data types which improves the accuracy of predictions. Hadoop platforms provide highly scalable platforms and can store a much greater volume of data at lower cost. The purpose of the proposed Project is to help in identifying “at risk” students who are not progressing towards graduation early in order to get them back on track. The cause of lack of adequate progression can be identified and addressed. The system proposed will be helpful for educational decision-makers to reduce the failure rate among students. The implementation is done in Hadoop framework. The PAMAE algorithm is implemented for analyzing student’s academic data.