The Design And Implementation Of Voice Guide Data Warehouse Based On Big Data

With the rapid development of the mobile Internet,how to stimulate people's enthusiasm for outdoor sports is especially important.At the same time,with the improvement of the functions of Internet products,Internet companies collect a large amount of data,how to properly store and effectively mine these data has become the focus of the enterprise.The method of storing management data through the data warehouse came into being,providing the enterprise with the functions of analysis and decision-making.However,the traditional data warehouse technology is flawed and cannot meet the needs of Internet enterprises for data warehouse.The voice guide data warehouse provides decision-making basis for enterprises,and better manages and mines data for companys.First introduced the background and significance of voice guide and data warehouse.Through the research on the research situation at home and abroad,combined with the existing data development and processing technology,the demand analysis and system design of data warehouse based on voice guide is proposed.The entire system can be divided into foul modules: data processing platform,data modeling,web visualization system and data warehouse management.Data processing platform uses Hadoop distributed file system to store data.Use Yarn as a resource manager.Simplify data processing with HIVEQL and SparkSQL.Ralph Kimall's dimensional modeling approach is used in data modeling,solve the problem that the Internet industry needs fast delivery and agile iteration to ensure the data quality of the data warehouse.The method of tiered storage data is used to clean the data warehouse and improve the ease of use.The Web visualization system uses the SSM framework as a back-end architecture,and use Echarts data visualization technology to visualize the most valuable data to businesses and users.The data warehouse of big data has been run on-line in the enterprise,and help data warehouse developers to develop data more efficiently and ensure data quality.Let data better for businesses and users.