High availability and fault tolerance design of big data analysis platform

In response to the fact that big data analysis platforms often only focus on certain specific aspects of high availability and fault tolerance, and their availability and fault tolerance are relatively low, this article takes power data as the object, combines cloud computing to build a big data analysis platform, and conducts research on the optimization design of platform availability and fault tolerance. Firstly, relevant experimental data such as equipment status data and power supply network data of a certain power company can be collected. Then, in order to meet the needs of high availability and fault tolerance of the platform, cloud computing multi area deployment and elastic resource management can be adopted, and load balancing technology can be used to evenly allocate requests to multiple nodes. By utilizing fault-tolerant routing mechanisms, faulty nodes can be detected and bypassed in a timely manner. Finally, a power big data analysis platform that integrates cloud computing can be constructed, and the high availability and fault tolerance of the experimental platform can be verified. The experimental results show that the platform for cloud computing based big data analysis optimization only takes 0.68 seconds to recover from failures, which is 1.27 seconds less than traditional big data analysis platforms. The platform data integrity is as high as 98.60%, indicating that the experimental platform has high availability and fault tolerance.