Design and Implementation of a Distributed Technology-Based Big Data Analysis System

A new big data analysis method for big data is proposed to address the storage, processing, and security issues of massive data in the big data environment. The main research objects are Hadoop HDFS and Spark, with scalability, fault tolerance, and efficiency as the goals. A hierarchical approach is adopted to achieve data collection, storage, processing, and visualization integration. On this basis, it is proposed to improve the overall performance of the system through data block storage and parallel processing, achieving high-speed, efficient, and robust fault tolerance. The system also incorporates security measures for data protection and integrity across multiple nodes. Performance testing reveals that the system excels in data handling, resource allocation, and fault tolerance across various data scales, while identifying areas for optimization. The research results of this project will provide valuable references for practical problems in the field of big data, and also provide new ideas for the scalability development of big data.