Real-Time and Offline Analytics for E-Commerce: A Hybrid Approach Using Apache Kafka, Apache Flink, and Machine Learning

In the rapidly evolving digital landscape, optimizing user engagement on e-commerce platforms is crucial for success. This paper presents a hybrid analytics system integrating real-time and offline analysis to enhance the functionality and user experience of a hypothetical furniture website. Utilizing Apache Kafka for data streaming, Apache Flink for real-time processing, and MongoDB for offline storage, the system analyzes user interactions and preferences. Machine learning models, including Random Forest and XGBoost, are employed for predictive analytics, with differential privacy ensuring data security. The system also incorporates Explainable AI for transparency. Realtime visual analytics via Elasticsearch and Kibana empower stakeholders with actionable insights, fostering strategic decision making and improved customer satisfaction. This work demonstrates the transformative potential of combining real-time data processing with in-depth offline analysis in the e-commerce sector.