Design And Implementation Of Real-time Data Warehouse Visualization System Based On Flink

The purpose of this paper is to build a complete real-time data visualization system.The development of the Internet is accompanied by the growth of visits and transactions,and the data shows an explosive growth.Traditional databases cannot meet the needs of data storage,and visualization technology The real-time data that meets the needs of enterprises cannot be displayed well.This paper designs and implements a real-time data visualization system based on Flink,which is used to respond to the demand for real-time visualization of massive data to support enterprises' decision-making.The main task is to build a big data platform to complete the six core modules of data transmission,data storage,ETL,data modeling,real-time analysis and processing,and data visualization.We store the data in HDFS through the built transmission channel,and transfer it to the Hive data warehouse through ETL.According to the actual business needs of the enterprise,the data stored in the data warehouse is modeled and hierarchically processed,and complex tasks are decomposed into multiple layers to complete and increase the reusability of a calculation result.Use Flink to calculate and process data in real time,store the data in the top level of the data warehouse according to business needs,and finally export it to the My SQL database,and then use the ECharts visualization tool to visually display and analyze the data.In addition,the system also integrates the Azkaban task automatic scheduling system to solve the problems of difficult maintenance and difficult upgrades.This article also built a cluster quality monitoring and metadata management module,which can observe the cluster status in time to prevent node crashes and data loss,and greatly upgrade the availability and maintainability of the entire system.In addition,this article also customized visualization software for multi-screen display scenarios,so that real-time changes of data can be observed on multiple screens at the same time,so as to conduct a comprehensive comparison of time and space dimensions,which can better support analysis.Finally,verify and test the system in the actual production environment.The results show that data transmission will not be stuck even under peak conditions,and the data storage and ETL functions are complete.The visual display part can well meet the needs of large-screen analysis,so that data management and analysis are applied to the maximum,thereby supporting decision-making.