Integrating Legacy Systems with Modern Microservices
Abstract
The integration of legacy systems with modern microservices architectures has become a critical challenge for organizations aiming to achieve digital transformation while preserving existing investments. This paper presents a comprehensive approach that combines RESTful web services with AI-based data mapping techniques to enable seamless interoperability between outdated monolithic systems and scalable microservices environments. Legacy systems, often constrained by rigid data schemas, proprietary protocols, and limited scalability, hinder organizational agility and innovation. To address these challenges, the proposed framework utilizes RESTful APIs as a standardized communication layer, enabling loosely coupled interactions and facilitating real-time data exchange across heterogeneous platforms. Furthermore, the incorporation of artificial intelligence, particularly machine learning-based data mapping models, automates the transformation and alignment of legacy data structures with modern microservice schemas. This significantly reduces manual intervention, minimizes integration errors, and enhances data consistency. The methodology includes intelligent schema matching, semantic data interpretation, and adaptive learning mechanisms that improve mapping accuracy over time.
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