Optimizing the Integration of Enterprise Data Warehouses and Large Language Models

With the in-depth advancement of the digital transformation strategy, enterprise-level data warehouses, as the core infrastructure for intelligent decision-making, are confronted with technical bottlenecks such as insufficient dynamic expansion capabilities and low real-time analysis efficiency. However, the rapid development of large language model technology provides a new path for the intelligence of data processing. This paper systematically explores the integration logic and technical adaptability of the two types of technologies, revealing the feasibility of upgrading the data processing paradigm through model light-weighting technology. This study reviews the application efficiency of the existing system in scenarios such as real-time analysis and resource allocation from the perspective of technical synergy, and demonstrates the development trend of the converged architecture in directions such as trusted computing and multi-modal processing. This paper provides a theoretical framework and practical reference for constructing a new generation of data infrastructure that supports dynamic optimization and intelligent decision-making, and has guiding significance for promoting the release of the value of data elements.