• Version
• Download 3
• File Size 530.55 KB
• Download

Data Modeler’s Guide to Implement Kimball Dimensional modeling on Google BigQuery

Suhas Hanumanthaiah

suhas.h@hotmail.com

Abstract

In the era of cloud-native analytics, integrating classical data warehousing methodologies with modern platforms like Google BigQuery presents both transformative opportunities and critical design challenges. This paper explores the practical application of Ralph Kimball's dimensional modeling approach within the BigQuery ecosystem, offering a comprehensive guide for data modelers to build scalable, performant, and intuitive analytics solutions. Kimball’s methodology—centered around star schemas, fact and dimension tables, conformed dimensions, and slowly changing dimensions—has long been regarded for its simplicity and business alignment. However, adapting it to BigQuery requires a re-examination of implementation strategies due to the platform’s serverless architecture, columnar storage, and lack of enforced referential integrity. This research bridges traditional dimensional modeling principles with the unique characteristics of BigQuery by providing detailed recommendations on schema design, partitioning, clustering, and surrogate key generation. It also delves into advanced capabilities such as materialized views, BI Engine acceleration, support for nested and semi-structured data types, and dynamic value banding using template views and query parameters. By aligning data modeling best practices with BigQuery’s performance architecture and operational nuances, this guide empowers data architects to implement robust data warehouses that facilitate high-performance querying and business intelligence at scale. The insights presented here are grounded in both technical rigor and practical applicability, making them invaluable for modern cloud data initiatives.

Keywords

Dimensional Modeling, Google BigQuery, Star Schema, Data Warehouse Architecture, Business Intelligence

DOI: 10.55041/ISJEM00136