Hira Lal Bhandari*, and Roshan Chitrakar
With rapid and multi-dimensional growth of data, Relational Database Management System (RDBMS) having Structured Query Language (SQL) support is facing difficulties in managing huge data due to lack of dynamic data model, performance and scalability issues etc. NoSQL database addresses these issues by providing the features that SQL database lacks. So, many organizations are migrating from SQL to NoSQL. RDBMS database deals with structured data and NoSQL database with structured, unstructured and semi-structured data. As the continuous development of applications is taking place, a huge volume of data collected has already been taken for architectural migration from SQL database to NoSQL database. Since NoSQL is emerging and evolving technology in the field of database management and because of increased maturity of NoSQL database technology, many applications have already switched to NoSQL so that extracting information from big data. This study discusses, analyzes and compares 7 (seven) different techniques of data migration from SQL database to NoSQL database. The migration is performed by using appropriated tools / frameworks available for each technique and the results are evaluated, analyzed and validated using a system tool called SysGauge. The parameters used for the analysis and the comparison are Speed, Execution Time, Maximum CPU Usage and Maximum Memory Usage. At the end of the entire work, the most efficient techniques have been recommended.
English 
Spanish 
Chinese 
Russian 
German 
Japanese 
Portuguese 
Hindi