AI-Assisted Query Optimization Techniques for Cloud Databases Supporting Hybrid SQL and NoSQL Workloads
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V6I4P108Keywords:
AI-Driven Query Optimization, Hybrid SQL-NoSQL Databases, Cloud Database Management, Adaptive Workload Optimization, Cost-based OptimizationAbstract
Data management in the modern era has changed with the introduction of the paying database solutions which include Database as a Service (DBaaS) with the use of cloud computing. Nevertheless, query optimization in distributed and hybrid systems is highly challenging because of the dynamic workloads, heterogeneity of resources and cross platform data integration. By virtue of being based on standard rules and costs, traditional approaches to rule-based and cost-based optimization do not scale, and cannot easily adjust to the complexity and volume requirements of cloud-based and hybrid SQL-NoSQL databases. The present paper is based on the idea of the use of Artificial Intelligence (AI) and Machine Learning (ML) to optimize queries in a data-driven, adaptive, and autonomous manner. Supervised and unsupervised learning AIs that are used as optimizers are applied to learn cost models, predict optimal execution plans, and dynamically respond to changes in the workload, whereas workload forecasting and resource allocation is possible using deep learning (DL) models such as LSTMs and Transformers. Comparative study of SQLs and NoSQLs system shows that consistency and scalability have a complementary relationship, which makes it crucial to have hybrid system in the cloud ecosystem. The research finds that AI-based query optimization not only enhances efficiency, performance, and adaptability but also preconditions future autonomous and self-tuning database systems, which can work perfectly well in multi-cloud environments
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