Data Modeler’s Guide to Implement Kimball Dimensional modeling on Amazon Redshift

Authors: Suhas Hanumanthaiah

DOI: https://doi.org/10.37082/IJIRMPS.v10.i4.232639

Short DOI: https://doi.org/g9t996

Country: United States

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Abstract: This paper presents a comprehensive guide for implementing Kimball’s dimensional modeling methodology within Amazon Redshift, a fully managed cloud data warehouse platform. As enterprises increasingly adopt cloud-based analytics solutions, adapting proven data warehousing techniques to the unique architectural constraints and capabilities of Redshift becomes essential. The paper begins by outlining the foundational principles of Kimball dimensional modeling, including star schema design, the use of fact and dimension tables, surrogate keys, conformed dimensions, and slowly changing dimensions. It then explores how these concepts can be practically applied within Redshift’s distributed architecture using best practices in table design, such as the optimal use of sort keys, distribution styles, and compression encoding. Specific attention is given to leveraging Redshift’s features—like columnar storage, SUPER data types for handling semi-structured data, and materialized views—to enhance query performance and support modern BI requirements. The guide also addresses common challenges, such as the lack of enforced referential integrity and how to overcome them with robust ETL/ELT processes. By combining theoretical modeling concepts with actionable Redshift-specific strategies, this research empowers data modelers, architects, and engineers to build scalable, performant, and maintainable data warehouse solutions. Ultimately, it bridges the gap between classical BI methodologies and modern cloud-native data infrastructure.

Keywords: Dimensional Modeling, Amazon Redshift, Star Schema, Data Warehouse Architecture, Business Intelligence

Paper Id: 232639

Published On: 2022-07-09

Published In: Volume 10, Issue 4, July-August 2022

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