Architecting Intelligent Big Data Analytics Infrastructures for Scalable Knowledge Discovery, Enhanced Decision Support, and Real-Time Stream Processing Across Heterogeneous High-Velocity Data Environments
Keywords:
Big Data Analytics, Stream Processing, Decision Support Systems, Edge Computing, Federated Learning, Data Lakehouse, Scalable Architecture, Real-Time AnalyticsAbstract
The explosive growth of heterogeneous, high-velocity data in domains such as healthcare, finance, and industrial IoT has imposed new demands on big data analytics infrastructures. As of, the convergence of AI-driven analytics, edge computing, and real-time stream processing has necessitated the redesign of analytics architectures to ensure scalability, intelligence, and responsiveness. This paper presents a conceptual framework for architecting intelligent big data analytics infrastructures that enable scalable knowledge discovery, support advanced decision-making, and provide real-time processing capabilities across distributed, heterogeneous data environments. We review literature to establish foundational progress and analyze current architectural paradigms. The proposed model integrates AI-enhanced stream analytics, data lakehouse systems, and federated learning for real-time adaptive intelligence. Graphical representations, including a line chart and comparative tables, are presented to demonstrate the evolution of performance metrics and architectural components over time.
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Copyright (c) 2025 Kiran R (Author)
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