Analyzing Big Data Problem Using Hadoop and Cloud Technology

In any kind of industry sector networks they used to share collaboration information which facilitates common interests based information sharing. As this method decreases costs and increases incomes thus by sharing and processing data management challenges they develop their performance and security. We have developed a data intensive technique which is a method of service sharing in corporate networks through cloud based peer to peer data management platform. Already existing method Data Integration in Bigdata (DIB) integrates database management system, cloud computing and peer to peer technologies and found a flexible and scalable data sharing service network applications. There are many different areas need to be included and concentrated the split up of data to be dealt whenever user includes new set of data. As it have several split ups data should be properly fetched without any loss of information. Corporate network eliminates less efficient hadoop tool thus reduced total intercompany costs. In our proposed system Data Integration in Bigdata (DIB) is used for integrating data and enhances the model pay for efficient storage. Robustness of data, performance upgrade for size of data increase for prolonged storage use. The benchmarking results show that our method outperforms HadoopDB, a recently proposed large-scale data processing system, in performance when both systems are employed to handle typical corporate network workloads. The benchmarking results also demonstrate that our method achieves near linear scalability for throughput with respect to the number of peer nodes.