Benchmarking of SQL and NoSQL performance on database management solutions and their respective data organisation frameworks

Relational databases have been the predominant data management system for many years due to their ability to provide structured data storage and transactional integrity. However, the emergence of big data and web-scale applications has led to the need for more flexible and scalable database solutions. NoSQL databases have become a popular alternative, offering high availability and horizontal scalability for large volumes of unstructured or semi-structured data. This paper explores the rationale behind the NoSQL movement, including the rise of big data and the limitations of relational databases in clustered environments. It outlines several NoSQL data models, such as key-value, document, column family, and graph databases, which sacrifice some data consistency for scalability and availability. Features such as flexible schemas, MapReduce views, and the Cloudant query language provide more appropriate tools for this semi-structured data. The different use cases, architectures, and trade-offs between these database paradigms are compared and show that NoSQL databases are suitable for large-scale, distributed systems requiring flexible schemas and high scalability.