Building Resilient Enterprise Platforms Using Artificial Intelligence Event-Driven Microservices and Intelligent Data Engineering
DOI:
https://doi.org/10.15680/jd8yzt56Keywords:
Artificial Intelligence, Event-Driven Microservices, Intelligent Data Engineering, Enterprise Resilience, Distributed Systems, Self-Healing PlatformsAbstract
Modern enterprise systems operate in volatile, high-throughput environments where system resilience, adaptability, and minimal latency are critical business requirements. This paper investigates the strategic architectural convergence of Artificial Intelligence (AI), Event-Driven Microservices, and Intelligent Data Engineering to build fault-tolerant and adaptive enterprise platforms. Monolithic and tightly coupled request-response architectures frequently suffer from single points of failure, resource bottlenecks, and rigid scaling constraints. By transitioning to asynchronous event-driven microservices governed by event brokers like Apache Kafka, organizations achieve structural decoupling, high availability, and horizontal elasticity. Integrating Intelligent Data Engineering ensures that real-time data streams are continuously cleaned, transformed, and ingested into cognitive AI models. These AI components, ranging from predictive anomaly detection to automated self-healing mechanisms, transform passive event streams into active operational intelligence. Empirical results indicate that this integrated ecosystem significantly decreases mean time to recovery (MTTR), optimizes resource consumption during peak traffic, and reduces system downtime. However, adopting this complex architecture introduces trade-offs, including eventual consistency challenges, distributed tracing overhead, steep operational learning curves, and higher initial setup costs. Ultimately, this research offers an actionable design framework for enterprise architects seeking to deploy resilient, self-optimizing platforms.
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