Scalability of Snowflake Data Warehousing in Multi-State Medicaid Data Processing
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I3P107Keywords:
Snowflake, Medicaid data processing, Data warehousing, Scalability, Cloud analytics, Multi-state healthcare, Data integration, ETL pipelines, HIPAA compliance, Performance optimizationAbstract
Snowflake data warehouse scalability determines how one manages the increasing complexity of multi-state Medicaid data processing. Medicaid data is intrinsically complex and includes numerous various eligibility criteria, service delivery mechanisms, and reporting requirements depending on the state. The diversity as well as the volume of healthcare transactions and real-time processing requirements substantially limits conventional data systems. From this vantage point, Snowflake and other scalable cloud-based solutions offer disruptive possibilities. Especially skilled in controlling the three Vs of big data—volume, speed, and variability—Snowflake's design divides storage and computation while enabling elastic expansion. By allowing seamless data integration and parallel processing, it helps the Medicaid data from many states to be ingested, normalized, and virtually real-time without performance degradation. This abstract looks at a realistic implementation plan as well as a comparative case study incorporating Snowflake deployment among multiple state Medicaid systems. The strategy called for employing Snowflake's multi-cluster computing to effectively conduct concurrent processes, create coherent data schemas, and put safe data pipelines into action. Studies on data standardization across nations, cost forecasting, and processing speed indicated rather significant gains. Moreover, it lets analytics teams provide more agile, practical insights, so enhancing policy review and resource allocation. At last, Snowflake is a tool for modern, data-driven Medicaid administration able to adapt to changing healthcare demands and regulatory limits, not only a warehouse
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