Hybrid Big Data Architecture Using SQL, NoSQL, and Machine Learning: A Case Study on Classifying Electric Vehicle Types

Abstract

 This study presents a robust hybrid big data architecture that integrates both relational (SQL) and non-relational (NoSQL) database systems to effectively manage and classify diverse electric vehicle (EV) data. The suggested method makes use of each database type's advantages enables efficient storage, retrieval, and processing of heterogeneous datasets. The data was preprocessed and evaluated using data mining techniques. followed by the implementation of multiple machine learning techniques, such as AdaBoost, Random Forest, K-Nearest Neighbours, Decision Tree, Support Vector Machine, and Logistic Regression —for accurate EV type classification. Among these, Random Forest demonstrated superior execution with a precision of 99.99%. The trained model was deployed through a real-time, user-friendly web interface to facilitate practical application and accessibility. This approach highlights the advantages of hybrid data architectures and machine learning integration, providing a scalable and adaptable framework applicable to other domains requiring heterogeneous data management and intelligent classification.