"基于Hadoop的大数据处理与分析优化研究"

"Optimization of Hadoop File Archiving using LZO Compression: A Study for Software Engineering Students" Abstract: This bachelor's thesis explores the application of Hadoop architecture in big data processing and analysis. Through an in-depth analysis of Hadoop's principles and related technologies, the advantages and limitations of data storage, computation, and processing are discussed. Additionally, real-world case studies demonstrate the practical application and effects of Hadoop in various scenarios. Target Audience: This thesis is suitable for undergraduate students majoring in computer science and technology, software engineering, and those interested in big data processing and analysis. Scenario and Objectives: The aim of this thesis is to help readers gain a deeper understanding of the principles and applications of Hadoop architecture, as well as its advantages in big data processing and analysis. Readers can grasp the fundamental concepts, working principles, and core components of Hadoop through this study, understand its practical applications, and optimize configurations based on specific requirements. Additional Information: This thesis adopts a systematic research method, including literature reviews, theoretical analysis, and empirical studies, to ensure its scientific rigor and reliability. To maintain originality, strict plagiarism checks have been implemented to ensure that it has not been previously submitted and can pass plagiarism detection systems. Keywords: Hadoop architecture, big data processing, distributed computing, data storage, data analysis; Southwest University of Finance and Economics Bachelor's Degree Thesis on Optimization of Hadoop File Archiving using LZO Compression"