A Novel Coordinated Hybrid Model for Multi Paradigm Database

Abstract

NOSQL document databases emerged as an alternative to relational databases for managing large volumes of data. NOSQL document databases are ensures big data storage and high-quality query performance and essentials, when the data scheme does not fit into the scheme of relational databases. They store their data in the form of documents and can handle unstructured, semi-structured, and structured data. As a result of the vast number of data that is being obtained from digital users by various businesses and organizations, the advancement of technology has resulted in the building of massive data repositories, which are collectively referred to as "big data." through the extraction of hidden data from sizable datasets, a technology known as data mining is utilized to discover particular patterns and rules. The term "big data" refers to the total amount of data adjoining each stage of the human lifecycle. It asserts the idea that any sector that performs tasks can be listed and recorded in a series of data that is expanding extraordinarily quickly. The production of a large quantity of data at a speedy rate is the main issue. Due to the variety of different types of data, companies must also store and process it efficiently (structured data, semi-structured data, and unstructured data). This work evaluates the top open-source NoSQL document databases: pig, hive, NoSQL, and MongoDB, which has become a standard for nosql database evaluation. Test the architecture with different tools in terms of time required for read operation, scan and update on various different factors. The performance and scale-up of document databases are assessed using different workloads with a different number of records and threads, where the runtime is measured for each database. In the experimental evaluation, it was concluded that MongoDB is the database with the best runtime, except for the workload composed by scan operations.