Context-Preserving Data Vault Modeling for Generative Analytics Platforms

Abstract

As generative analytics platforms proliferate; enterprise data warehousing architectures are facing new and complicated demands. Traditional dimensional models like star and snowflake schema have shown significant drawbacks in supporting schema agility, temporal history, and contextual lineage at the scale demanded by today’s machine-learning-powered analytical pipelines. The present paper is a theoretic and experimental evaluation of context-preserving Data Vault 2.0 (DV 2.0) modelling as a substrate for generative analytics environment. The paper then assesses the structural, contextual, and operational benefits of DV 2.0 for generative workloads on the basis of an extensive literature review of the principles of data warehouse design, extract–transform–load (ETL) quality management, data lake integration, business intelligence (BI) evolution, and dimensional modelling best practices. The key quantitative benefits are: A 49% improvement in the audit trail traceability when compared to pre-2016 ETL implementations; ETL pipeline error rate went from 9.8% (star schema) to 4.1% (DV 2.0) by 2020; Ingestion throughput was improved by 158% compared to star schema; Data quality completeness score of 91.6% under DV 2.0 governance. In addition, the paper suggests a formal context-preservation index (CPI) formulation, and a multi-tier analytical pipeline architecture that is capable of hosting both batch and real-time generative workloads. Results suggest that DV 2.0 with satellite level metadata will be a better and more scalable building block for next generation analytics platforms.