Literature Review on Database Design Testing Techniques

Imam, Abdullahi Abubakar; Basri, Shuib; Ahmad, Rohiza; González-Aparicio, María Teresa

doi:10.1007/978-3-030-19807-7_1

Cited by 1 publication

(1 citation statement)

References 20 publications

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“…Main strength of Oracle relational database and MongoDB NoSQL database. [11] Google Big Table, Amazon SimpleDB, Apache CouchDB, MongoDB, Cassandra, Hbase [13] Cassandra, MongoDB, Couchbase [14] SQL, Cassandra, CouchDB, DynamoDB, MongoDB, GraphDB [15] MySQL, Oracle, SQL Server, PostgreSQL, Sybase, MongoDB, Redis [23] MongoDB [24] MySQL, MongoDB [25] SQL Server, MongoDB [26] MongoDB, MySQL [27] MongoDB, Oracle [28] MongoDB, RavenDB, CouchDB, Cassandra, HyperTable, CouchBase, MS-SQL Server Express [29] Oracle, Neo4j [30] Neo4j, Oracle [31] Oracle, MongoDB [32] MySQL, Neo4j [34] MongoDB, MySQL [36] SQL Server, In-memory TPC databases via HammerDB [37] MongoDB, MySQL [38] PostgreSQL, MongoDB, Neo4j [39] SQL and NoSQL databases [40] Oracle 12c, JSON, BSON, OSON [42] Open Source Graph Databases [43] PostgreSQL, H2 (Open Source lightweight Java RDMS), HBase, JanusGraph [44] Oracle 11g, MongoDB [46] Neo4j, MySQL [47] MongoDB, PostgreSQL [48] MongoDB, MySQL, VoltDB for IoT data used in sensor [49] MySQL, MongoDB [50] SQL to NoSQL MongoDB Migration [51] MySQL, MongoDB MySQL, MongoDB [54] MySQL, MongoDB [55] MySQL, MongoDB [56] MySQL to MongoDB transformation [58] MySQL (JDBC driver), Cassandra (Simba's Cassandra JDBC and ODBC) [59] MySQL, MongoDB [60] CouchBase, RethinkDB, MongoDB [61] MongoDB and Oracle NoSQL [62] Dynamo (Amazon), Voldmart (LinkedIn), Redis, BerkeleyDB, Riak, Mo...…”

Section: Performance Comparisons Nosql Databasesmentioning

confidence: 99%

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

Khan

Kumar

Cheng

et al. 2023

BDCC

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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.

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