Abstract
Traditional data management practices replicate data across data centers and regions to ensure availability and performance. Such data replication can result in high operational costs, complex data management, inefficient resource utilization, etc. This disclosure describes techniques that address the inefficiencies and complexities of existing cloud storage and data access mechanisms by leveraging the simplicity and efficiency of symbolic links for data tables and by dynamically optimizing data access based on usage patterns discovered using machine learning. Unnecessary data replication is reduced, leading to simplified data management, efficient resource utilization, and lower operational costs. Data access in distributed environments is made scalable and evolves with usage patterns and computing environments. Administrators can more effectively comply with data governance and regulatory requirements.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Mahajan, Puneet, "Optimizing Data Access Using Symbolic Links and Machine Learning", Technical Disclosure Commons, (March 12, 2024)
https://www.tdcommons.org/dpubs_series/6782