Migration of Relational Database to MongoDB and Data Analytics using Naive Bayes Classifier based on Mapreduce Approach
Ganesh B. Solanke, K. Raja Rajeswari · 2017
Traditional databases are inadequate to catch up with the growing demands of big data handling and processing. Conventional relational databases have data model limitations, architectural limitations, scalability and performance limitations. New generation NoSQL database has features like schema-less design, high availability, location independence, scalability which makes it efficient to handle growing data management demands. Today many organizations are adopting MongoDB for faster application development and handling of highly growing multi-diverse data efficiently. In this paper, we propose an efficient approach for migration of data from MySQL to MongoDB and Naive-Bayes classification Mapreduce model on migrated data. In this paper, we have also evaluated migration and classifier on two different datasets. Experiment results show that the query execution performance is improved after migration. Naive Bayes classifier Mapreduce model save considerate amount of time and give better results on migrated data.