HEALTHCARE DATA STORAGE USING BIGQUERY

The implementation of a healthcare data storage system using Google BigQuery provides an efficient, scalable, and secure solution for managing large volumes of healthcare data. Traditional systems face limitations in handling rapidly growing datasets, whereas the proposed system leverages cloud technology to overcome these challenges effectively.The system works in real time, which helps in processing information quickly and running queries without delay. It can also be easily connected with analytical tools, making it easier to take better decisions and improve healthcare services. Features like automatic data collection, high availability, and fault tolerance ensure that the system runs smoothly without interruptions. At the same time, security measures such as encryption and access control are important to keep sensitive patient data safe.Based on these observations, using BigQuer for data storage proves to be more effective than traditional methods. It simplifies data management and makes it easier to handle large amounts of data while supporting efficient analysis. In conclusion, adopting BigQuery for healthcare data management can improve both research and service delivery, making it a strong and modern solution for the healthcare sector.  The use of BigQuery provides a modern solution to these challenges by offering a scalable, fast, and reliable platform for storing and analyzing healthcare data. It allows organizations to process large volumes of data quickly and supports real-time analysis, which helps in better decision-making and improved patient care. In addition, features like high availability, automated data handling, and strong security measures ensure that the system remains reliable and protects sensitive information. Overall, adopting BigQuery for healthcare data storage improves efficiency, supports advanced analytics, and helps healthcare organizations meet the demands of modern data management.

1.                Google Cloud, BigQuery Documentation, Available: https://cloud.google.com/bigquery 2.  Dean, J., & Ghemawat, S., “MapReduce: Simplified Data Processing on Large Clusters,” Communications of the ACM, 2008. 3.    Apache Software Foundation, Apache Hadoop,     Available: https://hadoop.apache.org   4.  Raghupathi, W., & Raghupathi, V., “Big Data Analytics in Healthcare: Promise and Potential,” Health Information Science and Systems, 2014. 5.  Wang, Y., Kung, L., & Byrd, T. A., “Big Data Analytics: Understanding its Capabilities and Potential Benefits for Healthcare Organizations,” Technological Forecasting and Social Change, 2018. 6.   IBM, What is Big Data in Healthcare?, Available: https://www.ibm.com 7.   Kuo, M. H., Sahama, T., Kushniruk, A. W., Borycki, E. M., & Grunwell, D. K., “Health Big Data Analytics: Current Perspectives, Challenges and Potential Solutions,” International Journal of Big Data Intelligence, 2014. 8.  Chen, M., Mao, S., & Liu, Y., “Big Data: A Survey,” Mobile Networks and Applications, 2014. 9.    Microsoft Azure, Cloud Computing in Healthcare,           Available: https://azure.microsoft.com 10.      OECD, Health Data Governance: Privacy, Monitoring and Research, OECD Publishing, 2015