Model-Driven Data Engineering Approaches for Automating Schema Evolution and Cross-Platform Data Integration in Heterogeneous Systems
Keywords:
Model-driven engineering, schema evolution automation, cross-platform data, heterogeneous data systems, data integration frameworks, metamodel-based designAbstract
Modern data ecosystems are increasingly composed of heterogeneous systems with diverse data models, storage formats, and access paradigms. As these systems evolve, maintaining seamless integration and consistency across platforms becomes a major challenge. Model-driven data engineering (MDDE) offers a promising paradigm to automate schema evolution and cross-platform data integration by leveraging high-level abstractions, metamodeling, and transformation rules. This paper explores how MDDE enables the automation of schema evolution processes and promotes interoperability across platforms in heterogeneous environments. We present a structured approach for integrating data across disparate systems using metamodels and mapping rules, and propose a system architecture that supports dynamic schema adaptation. The contribution includes a synthesis of current model-driven practices, a review of existing solutions prior to 2023, and a prototype framework. Experimental evaluation demonstrates the effectiveness of the proposed method in reducing manual overhead and improving data consistency across evolving platforms.
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Copyright (c) 2023 Martin Lee Victor (Author)
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