Industrial Data Integration And Application Based On Kettle

In recent years,the merging of the internet and industry has resulted in the creation of an enormous volume of data.The digital transformation has resulted in the production of heterogeneous data at various stages of the industrial value chain.This includes simulation data in engineering,sensor data in manufacturing,and telemetry data in product usage.Extracting value from this data is the primary task for industrial enterprises to succeed.Extracting useful data from the generated industrial data for big data analysis and applications is currently a challenge.This thesis uses data integration to eliminate data redundancy and contradiction,improve data quality,realize data sharing and exchange,and enable data to better support business operations and decision analysis of enterprises.Traditional data integration technologies suffer from issues such as high complexity,high error-proneness,low performance,and poor scalability,which can significantly limit the efficiency and stability of data integration in practical applications.Compared to traditional integration technologies,Kettle can more efficiently accomplish data integration and transformation tasks.Due to the differences in quality,collection,application,and interoperability between industrial data and other data,the integration process of industrial data is also different.This thesis designs an integration process suitable for industrial data,which solves the problems existing in existing applications.The research results of this thesis are as follows:Based on an analysis of the unique characteristics of industrial data,approaches and methods such as industrial data fusion,data transformation,and integration processes have been suggested to specifically address the challenges of integrating industrial data.As a result,the efficiency of sharing and utilizing industrial data has been significantly improved.To ensure consistency between the original data source and the integrated data,as well as timely updates,a proposal has been made to add update mechanisms to both the original data source and the integrated data,namely by using a real-time scheduling strategy to achieve data synchronization.Design and develop a real-time scheduling website system.The system is used for real-time scheduling and monitoring of jobs and transitions created by Kettle.This thesis selects self-developed Java programs to invoke the Kettle class library to achieve more flexible and complex task scheduling functions.Real-time scheduling technology has overcome the challenges of Kettle’s lack of mature scheduling and tracking capabilities,improving information integration efficiency,and reducing project development and operational costs.This thesis uses Kettle to create ETL programs to clean and load industrial data into a big data platform.Through a real-time scheduling system,industrial data is continuously updated to ensure data availability.Finally,usable data is provided to users through business views.The proposed integrated processes of data analysis,data extraction,data loading,and data updating have been successfully applied in the economic and information commission’s large-screen project.The results show that this integrated process can solve the integration problem of industrial data.