Design And Implementation Of Business Intelligence Platform Based On Big Data Processing Technology

In the early stages of enterprise development,data analysis was generally performed by executing queries on a direct back-end database or performing data calculations on a single node.With the continuous development of business services,the amount of data is increasing,the complexity of data is continuously increasing,and business parties are increasingly demanding data support.The original data analysis method has been difficult to meet the needs of enterprise development in terms of performance.In order to provide enterprises with the necessary data support,data analysis needs to use big data processing technology to complete data storage and calculation on a distributed cluster.In response to these problems,the Hive data warehouse was built on the Hadoop and Spark big data clusters through the Kimball modeling technology,which solved the problems of high data complexity and long data analysis time.The data warehouse constructs a unified data analysis source for the enterprise by performing ETL(Extraction-Transformation-Loading)processing on various data sources of the enterprise.The metadata management system provides functions such as data dictionary,index management,and kinship management through data warehouses and application data sources,and ensures the timely updating of metadata through a well-designed process.It improves data quality and significantly improves the speed with which newcomers can get started.At the same time,a report platform based on Superset was developed to achieve data presentation.Solved the problem of scattered data analysis.In technology selection,the data warehouse modeling adopts the Kimball dimension modeling technology.In Hive,the zipper table and data change acquisition are realized,and the data processing method is proposed for unstructured buried data.The development of the ETL process is achieved using a combination of ETL tools and manual development.The reporting platform was built on the Apache Superset data visualization tool and was extended using a front-end framework such as G2.The metadata management system collects metadata using a combination of manual entry and automatic resolution.Currently,the BI platform has been put into operation and is operating well.It has not only reduced the development of data analysis,but also significantly improved the efficiency of data analysis.It has provided good data support for all departments of the company and has been highly evaluated by the company's management.In this process,we established and improved a basic data framework,which solved the problem of inconsistent indicator metrics,high repetitive calculation rates,and eliminated data islands and chimney-like data marts.