Design And Implementation Of E-Commerce Big Data Process Platform

With the deepening and popularization of digitalization,more and more people shop online,resulting in massive data generated by e-commerce platforms.How to deal with data efficiently,mine the business information contained in the data and its complex relationship,and show it to enterprises in real time,in order to assist marketing,has become a topic that enterprises have been thinking about.Traditional data processing solutions have some shortcomings,such as incomplete function,unreasonable architecture design,weak timeliness and less use of algorithm-assisted marketing.To solve these problems,this paper analyzes the operating mechanism supporting big data marketing and its research status at home and abroad,designs and implements an e-commerce big data processing platform,and the main work includes:1.Completed the demand analysis of the big data processing platform,with core functions including data acquisition and storage,offline data processing,real-time data processing,large-screen data display,user portrait,etc.;2.Designed the solution of platform architecture and core modules.1)Designed an acquisition module that can be used for offline data processing and real-time data processing at the same time,which has the function of breakpoint acquisition and multi-point acquisition.2)An efficient data processing architecture is designed for real-time data to solve the problem of insufficient timeliness in real-time data processing under the background of massive data;Some techniques such as configuration separation and dynamic shunt are used to make up for the shortcoming of Flink operator in dynamic execution.3)A fully automatic scheduling method is designed for offline data,and indicators such as user details,user retention rate,number of active devices in each dimension,and number of newly added devices are calculated,which on the one hand serves as the basis for user portrait,and on the other hand serves as a supplement for large-screen display.3.In order to make the platform better support other businesses,such as recommendation system,marketing system,advertising,etc.,the user portrait system with custom rules is designed,and the machine learning algorithm is used to predict the information not provided by users,and finally a user label table is maintained.Business departments can combine different tags to group users and send corresponding product recommendations.After testing,the system in this paper runs well,and the results are consistent with expectations,which can provide help for enterprise marketing and decisionmaking.