Comparative Analysis of Hadoop and Snowflake in Handling Healthcare Encounter Data
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P106Keywords:
Hadoop, Snowflake, Healthcare Encounter Data, Big Data, Data Warehousing, ETL, Cloud Analytics, HIPAA Compliance, Data LakehouseAbstract
Improving patient outcomes and the operational efficiency in the era of digital health transformation depends on one's ability to effectively manage & evaluate massive healthcare data. Healthcare encounter data including thorough records of interactions between patients and the healthcare providers—is very vital in this set. Clinically, this data supports billing, policy development, care coordination, and these clinical insights. Big data platforms like Hadoop and Snowflake are becoming more and more important as companies struggle with best approaches for storing, analyzing, and extracting value from information. Two different approaches in big data management are Hadoop, recognized for its open-source flexibility and their distributed computing features, and Snowflake, unique for its modern cloud-native architecture and more seamless integration. This study compares many other systems with an eye on their efficiency in handling healthcare encounter information. Particularly with reference to healthcare needs, we look at performance metrics, scalability options, cost-effectiveness, and the data governance capabilities. We want to clarify the benefits and the drawbacks of each platform by means of an analytical modeling, technical benchmarking, and the practical case study from a data ecosystem of a healthcare provider. Our findings show clear differences: Snowflake shines in query speed, governance simplicity, and scaled-improvement in cloud environments; Hadoop offers resilience for unstructured data and cheap storage. The outcome emphasizes how the optimum choice depends on their specific healthcare data demands, infrastructure sophistication, and organizational goals; so, stakeholders should match platform capabilities with their long-term data strategy
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