Systematic Literature Review and Comparative Performance Analysis of SQL and NoSQL Databases in Big Data Applications

Systematic Literature Review and Comparative Performance Analysis of SQL and NoSQL Databases in Big Data Applications

연구 분야: Databases

학회: 2024 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

Today, in the digital moment, when the presence of the Internet in daily life is all around, management of data has become an indispensable part in any kind of organization irrespective of sector. Large-scale data applications are remarkably influenced in prosperity by the choice between SQL and NoSQL databases. This research deals with issues regarding which among database types- SQL or NoSQL-is more effective in Big Data environments. The paper consequently benchmarks the key performance indicators such as storage capacity, query execution time, and scalability through a systematic review of literature. SQL databases have been found to be robust with respect to structured data and complex queries, maintaining transactional integrity and consistency. They are, however, limited in scalability and flexibility when handling unstructured data, thus less suitable for specific big data applications. On the other hand, NoSQL databases support availability and flexibility quite well, handle unstructured data in very large volumes, and give support for a variety of data models. This study concludes that organizations have to align database choice with the type of data an organization deals with and scalability requirements for optimal performance in Big Data applications.