Multidimensional Data Analysis of Ambient Air Quality Based on Apache Kylin

Abstract

Abstract While the environmental monitoring department increases the number of monitoring points and the frequency of monitoring, it will also bring about a surge in the amount of monitoring data and computational response events. However, the traditional data statistics methods based on relational databases are ineffective in the face of huge environmental monitoring data. Aiming at ambient air quality data analysis, this paper uses Hadoop, Hive, Kylin, and other tools to build a multi-dimensional analysis platform for ambient air quality big data in a distributed environment, which realizes the unified storage, calculation, and analysis of ambient air quality monitoring data. Compared with the traditional relational database statistical analysis scheme, the proposed solution significantly improves the efficiency of statistical analysis of ambient air quality data under the condition of large data. The response time is shortened by 98%, reaching the sub-second level.