Insights into NoSQL databases using financial data: A comparative analysis.

The massive increase of data and the existence of several distinct methods in which data is generated, accumulated, stored, and utilized has significantly changed with time. Additionally, the nature of data has changed throughout the years transforming from structured to more unstructured data. This brings about a need for efficient storage and management of such data which cannot be handled by traditional RDBMS methods. Hence, NoSQL databases have gained popularity and have become pivotal in database management. Finance is one domain where dynamic and large amounts of data is produced on a daily basis, thereby making NoSQL databases an ideal choice for data management. This paper compares these types of NoSQL databases based on certain metrics like data model, indexing methods, atomicity, integrity and several more and demonstrate implementation of three NoSQL databases namely, MongoDB, Cassandra and Redis, using financial data. Experiments were performed to compare the performance of the aforementioned databases when using fundamental READ queries to retrieve the complete dataset and complex READ queries to retrieve a specific section. Aggregation operations were also implemented on the data. Fundamental WRITE queries to load the entire dataset and complex WRITE queries to update particular parts of it were also performed. [ABSTRACT FROM AUTHOR]