A Scalable Conceptual Framework for Distributed Heterogeneous Big Data Integration to Support Intelligent Decision-Making

Abstract

The exponential growth of heterogeneous big data generated from enterprise applications, cloud computing platforms, Internet of Things (IoT) devices, social media, and web services has created significant challenges in large-scale data integration and analytical processing. Traditional centralized integration systems often face limitations in scalability, processing efficiency, storage capacity, and fault tolerance when handling continuously increasing volumes of heterogeneous information. Distributed computing technologies provide an effective solution by enabling parallel data processing, scalable storage, and efficient resource utilization across multiple computing nodes. This paper presents a conceptual scalable framework for heterogeneous big data integration based on distributed computing technologies. The proposed framework conceptually integrates heterogeneous data collection, distributed storage, parallel preprocessing, intelligent data integration, metadata management, and centralized analytical services within a unified architecture. Distributed computing platforms such as Apache Hadoop and Apache Spark are considered as conceptual technologies for supporting scalable data processing and efficient integration of structured, semi-structured, and unstructured datasets. The framework aims to improve scalability, processing efficiency, interoperability, and decision support while maintaining flexibility for future expansion. Conceptual analysis indicates that distributed computing can significantly enhance heterogeneous data integration by reducing processing complexity, improving resource utilization, and providing reliable information for business intelligence, predictive analytics, and organizational decision-making in modern big data environments.