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Healthcare generates massive datasets from EHRs, claims, labs, and IoT devices, requiring efficient data warehousing for analytics and decision-making. Traditional on-premises systems struggle with scalability and heterogeneous data, while cloud-based platforms like Snowflake and Big Query offer elasticity, cost efficiency, and advanced analytics inte-gration. This review explores current architectures, methodologies, and challenges in healthcare data warehousing, fo-cusing on data integration, privacy, and performance. It also highlights the lack of independent comparative studies and stresses the need for standardization, automation, and rigorous evaluation to support effective adoption in healthcare.
Keywords:
Data warehousing, Snowflake, Big Query, Data integration, Interoperability, ETL, OMOP CDM, Healthcare analytics
Cite Article:
"Designing a data warehouse for healthcare analytics using snowflake and big query-A Review", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b5-b10, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509102.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator