Efficient Cloud-Based Healthcare Financial Analytics Using Snowflake and Dynamic Query Pruning

The rapid expansion of the Internet of Things (IoT) and rapid adoption of cloud computing have created higher demand for secure, scalable, and efficient data-processing solutions. However, existing healthcare financial analytics operations come with unique challenges, such as huge computational costs; inefficient query processing; and less security when carried in a cloud setting. Traditional methods are often rigid in terms of dynamic optimization, resulting in slower data retrieval speeds, extra storage overhead costs, and bottlenecks in performance for large-scale financial datasets. Furthermore, when it comes to encrypting financial information, conventional data encryption methods tend to compromise efficiency for the sake of security or the other way around due to limited flexibility to encrypt-on-the-fly. With the inability to enable dynamic pruning of irrelevant data during query processing, costs and time costs will be excessive, leading to overconsumption of resources. Thus, the absence of an adaptive framework for optimizing processing and cloud storage spells performance degradation in terms of both scalability and accuracy. To mitigate these challenges, this research proposes designing a Snowflake-based financial analysis system that applies the Dynamic Query Pruning (DQP) enhancement to achieve query optimization, reduce computational overhead, and support real-time analysis for healthcare financial systems. The study amalgamates hybrid cryptographic security, cloud-based storage, and machine learning-optimized solutions to allow accurate, secure, and scalable financial data analysis. From the experimental results, it can be inferred that the proposed model allows greater efficiency in data ingestion with quicker query execution and enormous cost savings in healthcare financial analytics. The findings shed light on how operability-on-cloud optimization techniques can be extended into the handling of large-scale financial datasets in the ensuring extra security and computational efficiency