Next-Generation Cloud Architectures for Real-Time Retail Data Processing

Vivek Prasanna Prabu

doi:10.36948/ijfmr.2020.v02i02.41027

Next-Generation Cloud Architectures for Real-Time Retail Data Processing

Author(s)	Vivek Prasanna Prabu
Country	United States
Abstract	The retail industry is experiencing a seismic shift driven by the need to analyze and act on data in real time. Traditional batch-oriented systems are ill-suited for the demands of modern retail operations that must adapt to consumer behavior, inventory fluctuations, and supply chain dynamics in real-time. Next-generation cloud architectures are emerging as critical enablers for this transformation, providing the scalability, agility, and speed required to process high-velocity retail data streams. Cloud-native tools, including managed Kubernetes services, event-driven data pipelines, and serverless compute models, are replacing monolithic, on-premise systems. These architectures support continuous data ingestion, streaming analytics, and real-time decision-making, empowering retailers to personalize customer experiences and optimize backend operations. Key technologies such as Apache Kafka, AWS Kinesis, and Azure Event Hubs enable low-latency data movement, while cloud data warehouses and lakehouses store and serve analytics-ready datasets. Furthermore, machine learning models trained and deployed in real-time environments allow for dynamic pricing, fraud detection, and demand forecasting. Retailers leveraging these modern architectures report higher customer satisfaction, faster inventory turnover, and more accurate demand planning. By decoupling services, using microservices, and deploying scalable compute on demand, these solutions ensure resilience and elasticity. Cloud-native architectures also support integration with IoT, mobile apps, and e-commerce platforms, enriching the data ecosystem and supporting omnichannel retailing. Security and compliance are integrated through identity management, encryption, and policy-driven data governance features. This paper explores the evolution of cloud infrastructure in retail, highlights core architectural patterns, and outlines real-world applications. It provides decision-makers with a roadmap to adopt and scale next-gen architectures for their retail platforms. Drawing upon best practices and academic insights, we examine how cloud strategies align with business goals. Ultimately, this white paper demonstrates that real-time retail data processing is not only a competitive advantage—but a necessity for modern retailers.
Keywords	Real-Time Data, Cloud-Native Architecture, Retail Analytics, Event-Driven Systems, Microservices, Streaming Data, Kubernetes, Serverless, Omnichannel Retail, Edge Computing
Field	Engineering
Published In	Volume 2, Issue 2, March-April 2020
Published On	2020-03-06
DOI	https://doi.org/10.36948/ijfmr.2020.v02i02.41027
Short DOI	https://doi.org/g9dhjf

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Next-Generation Cloud Architectures for Real-Time Retail Data Processing

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