Optimizing Database Performance for Large-Scale Enterprise Applications
Journal: International Journal of Science and Research (IJSR) (Vol.11, No. 10)Publication Date: 2022-10-05
Authors : Yash Jani;
Page : 1394-1396
Keywords : automated query optimizers; refactoring queries; parameterization; limiting result sets; caching; in-memory caching; distributed caching; hybrid caching;
Abstract
In the digital transformation era, large-scale enterprise applications are the backbone of many organizations. Efficient database performance is crucial for these applications to ensure quick data retrieval, seamless user experience, and robust backend operations. This paper explores advanced strategies for optimizing database performance, focusing on indexing, query optimization, caching, multithreading, and the utilization of NoSQL databases like MongoDB. By addressing these aspects, enterprises can enhance their database systems' scalability, reliability, and efficiency, ultimately driving better business outcomes.
Other Latest Articles
- Managing Data Sovereignty and Compliance in Multi-Cloud Environments
- Automated Blood Group Identification using Machine Learning and Deep Learning: A Novel Approach for Laboratory Settings
- Advancing Financial Fraud Detection: Exploring the Impact and Innovation of Deep Learning Models
- Enhancing Telecom Service Reliability: Testing Strategies and Sample OSS / BSS Test Cases
- Comparative Evaluation of Business Intelligence Platforms for Enhanced Decision-Making in Retail Organizations
Last modified: 2025-09-22 21:31:24