AI-augmented real-time retail analytics with spark and Databricks

Alva, Lingareddy (2025) AI-augmented real-time retail analytics with spark and Databricks. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1024-1033. ISSN 2582-8266

AI-augmented real-time retail analytics represents a transformative approach for modern retail operations, enabling businesses to process and act on data instantaneously in an increasingly competitive landscape. This comprehensive technical article explores the architecture, implementation, and business applications of an integrated analytics platform built on Apache Spark, Databricks, and Azure Event Hubs. The platform ingests data from diverse sources including IoT devices, point-of-sale systems, e-commerce platforms, mobile applications, and social media to create a unified view of retail operations. Advanced machine learning capabilities enable demand forecasting, customer segmentation, price optimization, and fraud detection with unprecedented accuracy. Large language models further enhance the platform by enabling natural language queries and automated insight generation, democratizing access to analytics across retail organizations. The business impact encompasses hyper-personalized customer experiences, predictive inventory management, revenue optimization strategies, and operational efficiency improvements. Implementation considerations and future trends are discussed, providing a blueprint for retailers seeking to leverage real-time analytics as a competitive differentiator in the age of artificial intelligence.