Data warehousing enables performant access to high-quality data integrated from dynamic data sources. The medallion architecture, a standard for data warehousing, addresses these goals by organizing data into bronze, silver and gold layers, representing raw, integrated, and fit-to-purpose data, respectively. In terms of data modeling, bronze layer retains the structure of source data with additional metadata. The gold layer follows established modeling approaches such as star schema, snowflake, and flattened tables. The silver layer, acting as a canonical form, requires a flexible and scalable model to support continuous changes and incremental development. This paper introduces an enhanced Hub Star modeling approach tailored for the medallion architecture, simplifying silver-layer data modeling by generalizing hub and star concepts. This approach has been demonstrated using Databricks and the retail-org sample dataset, with all modeling and transformation scripts available on GitHub.