Interoperability Challenges in Healthcare Data Lakes: A Snowflake-Based Approach

For providing patient-centered, coordinated, efficient treatment, data interoperability has become a basic demand in the changing sector of healthcare. Organizations are challenged by the growth of data from numerous sources—including electronic health records (EHRs), insurance claims, laboratory systems, and consumer wearables). Absence of standardization and isolated storage greatly limits real-time insights and inter-system communication. Data lakes are currently a decent approach to compile and maintain vast and diverse datasets at scale, but without consistent integration architecture their promise is not fully fulfilled. Introducing Snowflake, a contemporary cloud data platform meant to maximize data sharing, governance, and performance among numerous sources. Built on a decoupled storage-compute model and strong support for semi-structured data, Snowflake's design fits quite nicely for meeting the interoperability requirements of healthcare contexts. This article explores how Snowflake might form the basis for a healthcare data lake that takes data from various sources and aligns it to known standards using functionality including Snowpipe, data sharing, and safe data interchange. Using Snowflake's scalability, real-time data streaming, and natural support for analytics and machine learning, healthcare practitioners can cover ongoing data gaps—improving patient outcomes, expediting research, and lowering administrative inefficiencies. Our Snowflake-based method shows that, given the correct platform and approach, healthcare firms can go beyond fragmented systems to establish a connected ecosystem whereby data flows naturally, securely, and intelligently