Automobile Brand Analysis System Based on Feature Engineering and Apache Kafka+Flink Stream Data Processing Framework

With the rapid development of the automotive industry and the explosive growth of data, how to efficiently process and analyze automotive-related data to gain insights into automotive product trends has become an important issue. This paper presents an automotive brand analysis system based on feature engineering and the Apache Kafka + Flink stream data processing framework. Firstly, vehicle production and sales data, media data, and vehicle sensor data are collected to form a collection of multi-source heterogeneous data; secondly, through data cleansing, feature extraction, and feature transformation, feature engineering is constructed to create a standard, uniform, and norm-compliant data collection; then, based on the advantages and characteristics of Apache Kafka and Apache Flink, a stream data processing framework of Apache Kafka Flink is constructed, which can collect real-time data and efficiently process and transmit time-series data. Finally, by comparing traditional systems and single system architectures, it is concluded that the comprehensive performance of the system architecture presented in this paper is superior. The research aims to provide an efficient and precise automotive brand analysis solution for automotive enterprises and related research institutions.