ADAPTIVE MICROSERVICES ARCHITECTURES FOR MANAGING BIG DATA WORKLOADS IN ENTERPRISE ENVIRONMENTS

Abstract

The contemporary enterprise faces an unprecedented challenge: managing exponential data growth while maintaining operational agility. Traditional monolithic architectures create bottlenecks, hindering the scalability required for real-time analytics and global distribution. This paper examines the strategic adoption of microservices architecture as a foundational enabler for scalable data enterprises operating within cloud-native and hybrid infrastructure landscapes. By decomposing data processing and storage into discrete, loosely coupled services, organizations can achieve independent scaling, fault isolation, and technology polyglotism. Focusing on context of rising cloud costs, edge computing demands, and data sovereignty regulations, this paper synthesizes literature, provides architectural diagrams, and presents a comparative analysis of deployment models. The findings indicate that while microservices introduce operational complexity, their orchestration via Kubernetes and service meshes offers a superior pathway to data elasticity in hybrid environments.