Architectural Patterns and Performance Analysis of Integer Surrogate Keys for Time-Series Data Warehousing

This paper provides a comprehensive analysis of architectural patterns and optimization techniques fortime-series data processing, centered on the replacement of native DATE/TIMESTAMP types with integerbased surrogate keys. We demonstrate that employing 32-bit (YYYYMMDD) and 64-bit integer formats fordatetime representation, coupled with specialized algorithms for indexing, range search, and aggregation,yields substantial performance gains. Empirical evaluations confirm a 30–60% reduction in storagefootprint, a 25–40% acceleration in query execution, and up to an eightfold increase in system throughputthrough batched operations.Beyond these metrics, the study delves into advanced practical implementations across high-frequencytrading, telecommunications, and industrial IoT, detailing extended use cases such as real-time frauddetection and predictive maintenance. The paper further introduces a set of actionable implementationguidelines, including hybrid data models and optimized partitioning strategies, to facilitate adoption.Finally, we explore the application of International Atomic Time (TAI) to eliminate temporal ambiguitiesand outline future research directions integrating this approach with machine learning pipelines and edgecomputing architectures. The collective findings position integer-based timestamp storage as afoundational element in the design of high-performance, scalable, and reliable time-series datawarehouses.