ENGINEERING EVENT-DRIVEN MICROSERVICES PLATFORMS FOR REAL-TIME DATA PROCESSING IN CLOUD ECOSYSTEMS
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Abstract
The increase of cloud-native architectures has radically transformed the design, deployment, and scaling of distributed systems. Event-driven microservices have now become a prevailing paradigm of attaining high-throughput and low-latency data processing in dynamic cloud ecosystems. This article is a detailed study of the engineering concepts, design patterns, and technology frameworks that underpin the development of event-driven microservices platforms that are optimally geared towards real-time data handling. We discuss the architectural premises of asynchronous messaging, message broker technologies (especially Apache Kafka) and how container orchestration facilitates elastic scalability. Patterns such as Event Sourcing, Command Query Responsibility Segregation (CQRS) and the Saga pattern in managing distributed transactions are discussed in the paper. We also examine the issues of data consistency, fault tolerance, schema evolution, and observability in large-scale deployments. We base our analysis on existing theoretical foundations and reported industry application to offer practical design advice to architects and engineers to create next-generation cloud-native platforms. The results confirm that an appropriately designed event-driven architecture can help significantly decouple the operational characteristics, enhance system resilience, and provide real-time insights at scale in the contemporary cloud setup.
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References
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