🤖 AI Summary
To address the challenge of maintaining semantic consistency while enabling dynamic schema evolution in silver-layer modeling within data lakehouse environments, this paper proposes Hub-Star generalized modeling—a framework that abstracts the star schema paradigm into a unified, source-agnostic silver-layer modeling approach for heterogeneous, multi-source data. It supports incremental development and continuous schema evolution. Built on the Databricks platform, the method integrates Delta Lake’s ACID transaction capabilities, SQL-based declarative modeling, and metadata-driven automated transformations to ensure end-to-end reproducible modeling. Evaluated on a retail-org dataset, Hub-Star improves modeling efficiency by 40% and reduces schema change response time to the hour level. The implementation is open-sourced.
📝 Abstract
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.