Dynamic Cost Optimization Framework for BigQuery and Cloud Data Warehousing Systems

This dissertation introduces a cost optimization framework designed for BigQuery and cloud data warehouses, focusing on the issue of growing operational costs related to data processing and storage. Using extensive usage and performance data from various cloud environments, this research pinpoints key cost factors and formulates strategies for improved resource allocation, resulting in considerable cost savings. The results generally indicate that adaptive resource management methods can lower operational costs by as much as 30%, thus boosting the long-term financial viability of cloud data warehousing options. In healthcare, where budgetary limitations and data handling issues are common, these findings not only enable more cost-effective data usage but also boost the general effectiveness of healthcare analytics. Furthermore, the study's impact stretches beyond mere cost savings; it offers a model for incorporating economic factors into data-based decision-making within healthcare, with the potential to enhance patient results via more efficient resource deployment. In most cases, this research adds to a greater comprehension of the correlation between cloud economics and healthcare data management, providing useful recommendations for stakeholders aiming to fully exploit cloud technologies while keeping expenses under control effectively.