Chinnathambi, Jinesh Kumar (2024) Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration. European Journal of Computer Science and Information Technology, 12 (6). pp. 15-23. ISSN 2054-0957 (Print), 2054-0965 (Online)
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Abstract
Amplifying the utilization of big data in healthcare analytics through cloud and Snowflake migration presents a significant opportunity to enhance data-driven insights and decision-making in the healthcare sector. This migration makes it easier to move large amounts of healthcare data to the cloud. Applications deployed in could are scalable for in-depth analysis in Health Care industry. The cloud is becoming more popular for storing data and running applications because it can easily grow with your needs, requires little to no management, improves security, and offers budget flexibility. The benefits of the cloud are obvious -- once you get there. Moving to the cloud requires planning, strategy, and the right tools for data migration. [1] By using Snowflake's advanced data warehousing tools, healthcare organizations can smoothly handle and analyze their complex and varied data. This helps them quickly uncover important insights and make better decisions. The shift to cloud technology and Snowflake has the potential to significantly enhance real-time analytics, personalized patient care, and evidence-based decision-making in healthcare. When healthcare organizations leverage big data in a cloud-based setting, they can discover valuable insights from their data, ultimately improving clinical outcomes, operational efficiency, and healthcare delivery. This study explores how the adoption of cloud and Snowflake in healthcare analytics can bring about transformative change and create new possibilities for leveraging data and generating insights in the healthcare sector.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Depositing User: | Professor Mark T. Owen |
| Date Deposited: | 19 Sep 2024 14:26 |
| Last Modified: | 19 Sep 2024 14:26 |
| URI: | https://tudr.org/id/eprint/3385 |
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