Research On Distributed Multidimensional Analysis Method For Oil And Gas Drilling Information In Big Data Environment

With the rapid development of information technology in the drilling industry,the business data is increasing exponentially.The traditional drilling multidimensional analysis system has been unable to meet the efficiency of drilling data analysis nowadays.The emergence of Big Data technology provides a solution for the storage and calculation of data.This paper uses Hadoop technology and combines data warehouse and multidimensional analysis technology to design a distributed multidimensional analysis system for oil and gas drilling.It makes full use of a large number of drilling business data to analyze and provides support for decision makers to make Management.The work done in this paper includes the following points.This paper first designs the structure of the distributed multidimensional analysis system for oil and gas drilling information based on Hive,and the theme is determined based on the analysis of the characteristics of oil drilling production,and design the corresponding data model.Then using the Kettle and Sqoop technology,the data transfer and loading of HDFS and traditional drilling database are realized,and the data storage is optimized.It solves the problem of inconsistent data format and storage difficulty.Then,this paper give a distributed multidimensional analysis method based on Apache Kylin.Apache Kylin,Hive,and HBase are integrated.The multidimensional data model is designed and optimized.The second level speed query of drilling data is realized by optimizing the storage and query.This solves the problem of how to improve the speed of big data analysis.Finally,a distributed multidimensional analysis system for oil and gas drilling is designed based on the MyEclipse.The distributed multidimensional analysis system of oil and gas drilling is designed based on big data technology,which makes up for the shortcomings of the traditional data warehouse in the storage and processing of a large number of data,and ensures the efficiency andreliability of the query,it also has a good scalability.