| dc.description.abstract | Most large-scale modern applications are increasingly in need of database management
systems that are effective and scalable, meeting increased data complexity and volume
continuously. This study explores the programming and indexing methodologies
of SQL, NoSQL, and Hybrid Transactional/Analytical Processing (HTAP) systems,
assessing their suitability for large-scale databases. The objective is to compare the
structured, transactional strengths of SQL with the scalability and flexibility of NoSQL
for unstructured data, and to evaluate HTAP systems for handling both transactional
and analytical queries. Relevant studies published after 2015 were sourced from IEEE
Xplore, ACM Digital Library, and ScienceDirect, focusing on empirical performance
metrics. The findings highlight SQL’s strength in structured data processing, achieving
query speeds of up to 10ms for transactional workloads, while NoSQL excels in
unstructured environments with 20–30% faster query speeds. HTAP systems combine
features of SQL and NoSQL but face challenges in indexing strategies to optimize
diverse workload types. The review concludes that database selection should be context driven, optimizing for performance and scalability. The direction for future research
will be the empirical testing of HTAP systems in real large-scale environments for
the optimization of indexing strategies catering to both transactional and analytical
demands. | en_US |
