The contemporary data ecosystem is undergoing a profound transformation driven by the convergence of large-scale cloud-native data warehousing, artificial intelligence, and domain-specific analytics. Organizations operating in high-stakes environments such as healthcare, finance, energy, and industrial automation now depend on the seamless integration of data pipelines, scalable storage architectures, and intelligent analytical models to generate actionable insights. This research article advances a comprehensive theoretical and methodological framework for designing intelligence-driven data warehouses that fuse modern extract–transform–load paradigms, massively parallel processing platforms, and explainable machine learning systems. At the core of this framework is the recognition that data warehousing has shifted from being a passive repository of historical facts to an active computational substrate for continuous inference, prediction, and decision support, a transition that is particularly evident in cloud-native platforms such as Amazon Redshift (Worlikar, Patel, & Challa, 2025).

Building on this foundational understanding, the article critically examines how data engineering practices derived from data lakes, data warehouses, and hybrid architectures shape the reliability, scalability, and epistemic trustworthiness of downstream AI models (Mandala, 2021; Mandala, Data Engineering in Cloud-Native Architectures). It argues that the historical dichotomy between data lakes and data warehouses is increasingly inadequate for the complexity of modern analytics, particularly in domains such as predictive healthcare, financial fraud detection, and smart energy systems, where heterogeneous data streams, regulatory constraints, and real-time inference must be harmonized within a unified architectural vision (Yasmeen et al., 2024; Cheng et al., 2024). By integrating insights from explainable artificial intelligence, distributed machine learning, and multi-objective optimization, the article demonstrates that the architecture of the data warehouse itself is now a key determinant of model performance, interpretability, and ethical accountability (Xu et al., 2024; Soleimani et al., 2024).

Methodologically, the study adopts a design science research paradigm, combining architectural analysis, comparative evaluation of ETL strategies, and interpretive synthesis of recent advances in machine learning applications. Instead of relying on numerical experiments or benchmark tables, the analysis develops a richly elaborated conceptual model that links cloud-native data warehousing patterns, such as columnar storage, elastic scaling, and workload management, to the operational needs of intelligent systems across sectors. The descriptive results indicate that when modern data warehouses are designed according to principles of data-centric AI, governance-aware ETL, and explainable analytics, they significantly enhance the robustness, transparency, and adaptability of predictive models deployed in high-risk contexts.

The discussion situates these findings within broader scholarly debates on the future of data infrastructure, arguing that intelligence-driven data warehouses represent a new epistemological layer in digital societies, where the architecture of data storage and processing shapes what can be known, predicted, and acted upon. The article concludes by outlining future research directions that bridge data engineering, machine learning theory, and socio-technical governance, emphasizing that the next generation of data warehouses will not merely store data but will actively participate in the production of knowledge.

Cloud-native data warehousing, ETL pipelines, predictive analytics, explainable artificial intelligence

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