SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Abstract

The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. SQL databases were created to organize data and allow for horizontal expansion. NoSQL databases, on the other hand, support horizontal scalability and can efficiently process large amounts of unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting the best option is not always easy. Differences in database design are what set SQL and NoSQL databases apart. Each NoSQL database type also consistently employs a mixed-model approach. Therefore, it is challenging for cloud users to transfer their data among different cloud storage services (CSPs). There are several different paradigms being monitored by the various cloud platforms (IaaS, PaaS, SaaS, and DBaaS). The purpose of this SLR is to examine the articles that address cloud data portability and interoperability, as well as the software architectures of SQL and NoSQL databases. Numerous studies comparing the capabilities of SQL and NoSQL of databases, particularly Oracle RDBMS and NoSQL Document Database (MongoDB), in terms of scale, performance, availability, consistency, and sharding, were presented as part of the state of the art. Research indicates that NoSQL databases, with their specifically tailored structures, may be the best option for big data analytics, while SQL databases are best suited for online transaction processing (OLTP) purposes.

Accelerating materials research with a comprehensive data management tool: a case study on an electrochemical laboratory

“…Beyond that, there are more modern post-relational databases such as key-value, column, document-oriented, and graph databases which are more flexible in handling less structured data, making more versatile connections between data, and are easier to maintain upon changes of the data structures. 52 However, in the scope of designing data workflows for experimental setups with specified parameters for metadata and result data, a relational database is an appropriate choice. Considering researchers being familiar with data in tabulated form, the design process of a relational database schema is intuitive and supports a clear definition of a complete metadata set for the experimental setups.…”

Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System

“…In particular, NoSQL databases outperform SQL databases regarding writing speed and scalability. NoSQL databases perform better when dealing with large scalability requirements and facilitating rapid data updates [57]. However, SQL databases better manage complex relationships and multiple client scenarios [57].…”

“…NoSQL databases perform better when dealing with large scalability requirements and facilitating rapid data updates [57]. However, SQL databases better manage complex relationships and multiple client scenarios [57]. The characteristics of the SQL, structure, and capability to maintain data integrity make them suitable for scenarios involving relational data tables (such as the study carried out).…”

Big Data Storage based on CAN Protocol

“…The advent of Big Data [19] has also given birth to a series of technologies in data storage and NoSQL (Not Only SQL) is one of them [23]. NoSQL is a technology that is developed to solve the problems presented by relational databases.…”

“…In this section, we are interested in the NoSQL [23] database type with a Key-Value storage [26], which implements a schema-less storage policy. That is, a'no structure is formally defined for data storage.…”