Evaluating Advantages and Disadvantages of Proprietary SQL Extensions

This assignment explores the use of proprietary extensions in SQL databases, highlighting their role in providing functionalities beyond standardized SQL. It discusses the advantages, such as simplifying complex tasks and leveraging vendor-specific features to meet organizational requirements. The analysis also covers the disadvantages, including data migration challenges due to compatibility issues and the difficulty in finding developers skilled in various extensions. The paper acknowledges SQL's ease of use and its application in various development environments, from embedded systems to web applications, and emphasizes the importance of customer approval before implementing these extensions in database management system development. The assignment is supported by references to various research papers and publications.

Database Proprietary extensions
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Proprietary extensions in SQL are being used by developers and organizations to provide
additional functionalities that cannot be achieved with standardized SQL functions. These
functionalities range from business logics to database security (Arunkumar et al 2018, p. 23). As
organizations makes use of proprietary extensions in the development of the databases, it is
important to evaluate both advantages and disadvantages. Its advantages are; it helps both
developers and database users to achieve complex tasks more easily. The need for proprietary
extension is mainly driven by organizational requirements. Upon analysis of specific
requirements, database developers evaluates their ability to implement given requirements with
normal SQL. In cases where standardized SQL cannot be used to achieve organizational business
logics or security parameters, proprietary SQL extensions are adopted (Nemetz, Schmitt &
Freiling 2018, p. 126). Similarly, it is much possible to take advantages of vendor specific
features when using proprietary SQL extensions. Every extension has its own features that are
unique depending on its developer which makes it suitable for specific functionality. It is through
these extension vendor specifics that drive database developers to implement or impend them in
order to meet organizational specifications.
Despite advantages, there are some disadvantages that organizations and database
developers can face from using SQL extensions. First, when using other extensions apart from
standardized SQL, it is not possible to migrate any existing database to a different database
engine (Kondylakis et al 2018, p. 363). Data migration is a normal procedure undertaken by
organizations with aim of meeting daily operational need or when moving from one database
platform to the other. When SQL extensions are used, there are high possibilities that during data
migration, organization would face many challenges such as compatibility issues. Additionally,

since SQL extensions have unique features, it is very difficult to find SQL developers and
Database administrators who can work with variety of extensions.
In regard to Database management system (DBMS) development, use of SQL extensions
should not be implemented with approval from a customer. For DBAs and developers it is a
good idea because it is only meant to enhance database functionality as well as optimize query
processing (Liu et al 2018, p. 746). SQL is quite different from other programming languages
such as java and python. Both java and python are used in creating application interfaces (front
end) that communicate with DBMS. I really acknowledge SQL as a database development
engine that can communicate to other frond end development languages with just few
configurations. It is very easy and simple to learn with basic knowledge on its syntax. Finally, it
is a useful development engine considering its wide application of use in embedded and web
applications.
Bibliography
Arunkumar, A., Goutham, G., Narayan, L.V., Vasanthakumar, S. and Bhanumathi, M., 2018.
Performance Evaluation System by Scientific Workflow in Cloud. Journal of Computer Based
Parallel Programming, 3(1).pp. 15-56.
Kondylakis, H., Koumakis, L., Tsiknakis, M. and Marias, K., 2018. Implementing a data
management infrastructure for big healthcare data. In Biomedical & Health Informatics (BHI),
2018 IEEE EMBS International Conference on (pp. 361-364). IEEE.
Liu, Z.H., Czarlinska, A., McMahon, D.J. and Makur, A., Oracle International Corp, 2018.
Leveraging sql with user defined aggregation to efficiently merge inverted indexes stored as
tables. U.S. Patent Application 15/281,746.

Nemetz, S., Schmitt, S. and Freiling, F., 2018. A standardized corpus for SQLite database
forensics. Digital Investigation, 24, pp.S121-S130.