based onHadoopThe platform's bigData visualizationAnalysis, implementation and application
“Implementation and Application of Big Data Visualization Analysis on the Hadoop Platform”
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Article Directory
- Implementation and application of big data visual analysis based on Hadoop platform
- summary
- Chapter 1 Introduction
- 1.1 Research background
- 1.2 Purpose and significance of research
- 1.3 Research content and methods
- Chapter 2 Introduction to Hadoop Platform
- 2.1 Overview of Hadoop
- 2.2 The core components of Hadoop
- 2.3 Hadoop ecosystem
- Chapter 3 Basics of Big Data Visual Analysis
- 3.1 Overview of Big Data Visualization
- 3.2 Visual analysis tools
- 3.3 Visual design principles
- Chapter 4 Big Data Visual Analysis Implementation Based on Hadoop
- 4.1 Data acquisition and preprocessing
- 4.2 Data storage and management
- Chapter 5 Big Data Visual Analysis Application Cases
- 5.1 Case 1
- 5.2 Case 2
- Chapter 6 Summary and Outlook
- 6.1 Paper summary
- 6.2 Research Outlook
summary
This paper aims to explore and implement a big data visual analysis method based on the Hadoop platform, and conducts empirical research on its application. First, by buildingHadoop cluster, build an environment that can handle large-scale data. Then, Hive is used asData Warehouse, leverages its powerful query and analysis capabilities to process and extract massive data. For the extracted data, a set of visual analysis tools was developed based on the Hadoop platform. This tool intuitively displays the characteristics and trends of big data through charts, maps and graphics. Through practical application evaluation, the tool provides users with fast, intuitive and efficientData AnalysisMethods to effectively help users discover and understand the laws and relationships hidden behind big data. In short, this study implements a big data visual analysis method on the Hadoop platform and demonstrates its effectiveness and potential in practical applications. In the future, other visualization technologies and algorithms can be further explored to improve analysis effects and user experience, and provide more feasible solutions for big data analysis.