A Systematic Literature Review of Distributed Data Warehouse Architectures

Abstract

This research presents a systematic literature review (SLR) of distributed data warehouse (DDW) architectures, addressing challenges in governance, security, scalability, and real-time analytics. Conducted in accordance with PRISMA 2020 guidelines, the review synthesizes 29 peer-reviewed studies from 2020 to 2025. It identifies four major architectural themes: security-oriented, federated and data mesh–oriented, data lakehouse-based, and real-time/streaming-enabled architectures. These themes address recurring challenges such as data privacy, organizational autonomy, governance of diverse data types, and low-latency analytics. The review highlights the trend towards multi-paradigm designs that integrate multiple principles to balance autonomy, governance, performance, and security. Additionally, it outlines future research directions in autonomous architectures, AI-driven metadata management, and empirical evaluation of hybrid DDW models.