Data Storage and Analysis of Power Supply Service Grid Cloud Telephone System in Big Data Environment

In the big data environment, the data storage and analysis of the power supply service grid cloud telephone system face challenges of huge data volume and high processing speed requirements. In order to effectively address this issue, this study adopted cloud storage technology and big data analysis framework, combined with real-time data processing and batch data processing strategies, optimized the data storage structure, and achieved efficient querying and analysis of data. This study first adopts a distributed storage architecture and optimized data storage through the Hadoop Distributed File System (HDFS). Next, the article uses Apache Spark to achieve fast processing and analysis of data, improving the parallelism and efficiency of data processing. Finally, the article introduces Kafka to achieve real-time streaming processing of data, ensuring that data can be quickly analyzed and responded to while receiving real-time updates. The experimental results show that through the integration of this series of technologies, the storage speed of the system can reach up to 682 KB/s, and the data processing speed can also reach 800.0 KB/s, with an average error of only 1.2%. In the above data conclusions, combining distributed storage and real-time data processing technology can not only effectively solve the data storage and processing problems of power supply service grid cloud telephone systems in big data environments, but also improve the overall performance of the system.