A Polyglot Data Engineering Paradigm for Autonomous Multi-Modal Knowledge Fusion Across Heterogeneous Distributed Data Fabrics

Daniel Bringston

Authors

Daniel Bringston System Analyst, USA. Author

Keywords:

Polyglot data engineering, multi-modal fusion, distributed fabrics, knowledge orchestration, semantic integration, autonomous data pipelines, heterogeneous systems

Abstract

The exponential growth of multi-modal data across distributed infrastructures necessitates a paradigm shift in data engineering methodologies. This paper proposes a novel polyglot data engineering framework that enables autonomous knowledge fusion across heterogeneous data fabrics. By leveraging a multi-model, language-agnostic approach, this architecture supports scalable integration, contextualization, and real-time analytics across disparate systems and modalities. The proposed solution emphasizes semantic interoperability, adaptive orchestration, and cross-platform compatibility. Experimental validation demonstrates increased system agility, reduced latency in knowledge fusion pipelines, and improved coherence in decision-support systems. This research provides a critical foundation for future developments in intelligent, distributed data infrastructures.

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