A hybrid efficiency method for improvements in Big Data Hadoop

In recent years, database management system (DBMS) has become more prominent in the industry due to the staggering amount of structured and unstructured data.Dealing with management of such data is a challenge for DBMSs.According to several surveys, many company engineers choose Hadoop for their premier management system [5], due to its flexibility, ease of management and being open source software.However, more recently, database administrators of big company cite that Hadoop does not always work in their best interest in specific areas of data management.In order to solve big-data problems by providing considerable power of computability, Hadoop sacrifices efficient data processing.In the real-time data processing, Hadoop has lower speed than traditional DBMSs because it does not use high-level language, such as SQL and other query optimized languages.In this paper, we discuss methods to improve efficiency.Such improvements increase the ability of data processing in real-time, hence making Hadoop more useful.