BigData

BigData技术原理与应用(第2版) 本资源深入探讨了大数据技术的核心理论及其广泛应用。内容涵盖了大数据概念的阐释、存储方案的设计、处理方法的比较、分析技术的解读以及实际应用案例的解析。

BigData_DW_Real Document Overview The document BigData_DW_Real.docx provides an extensive guide on big data processing architectures, covering both offline and real-time processing architectures. Additionally, it details the requirements overview and architectural design of a big data warehouse project. Big Data Processing Architectures Big data processing architectures are primarily classified into two types: Offline Processing Architecture Utilized for data post-analysis and data mining applications. Technologies: Hive, Map/Reduce, Spark SQL, etc. Advantages: Capable of handling large volumes of data. Disadvantages: Slower processing speed, less sensitive to real-time demands. Real-Time Processing Architecture Suited for real-time monitoring and interactive applications. Technologies: Spark Streaming, Flink. Advantages: High responsiveness for time-sensitive data. Disadvantages: Faster processing but limited to simpler business logic. Big Data Warehouse Project Requirements The big data warehouse project encompasses six key requirements: Daily Active Users: Analysis with hourly trends and daily comparisons. Daily New Users: Analysis with hourly trends and daily comparisons. Daily Transaction Volume: Analysis with hourly trends and daily comparisons. Daily Order Count: Analysis with hourly trends and daily comparisons. Shopping Coupon Risk Warning: Function for identifying potential risks. Flexible User Purchase Analysis: Customizable analysis functionality. Architectural Design for Big Data Warehouse Project Main Project (gmall): Based on Spring Boot. Dependencies: Incorporates Spark, Scala, Log4j, Slf4j, Fastjson, Httpclient. Project Structure: Includes parent project, submodules, and dependencies. Technology Versions:- Spark: 2.1.1- Scala: 2.11.8- Log4j: 1.2.17- Slf4j: 1.7.22- Fastjson: 1.2.47- Httpclient: 4.5.5- Httpmime: 4.3.6- Java: 1.8

在CentOS 7系统中安装和配置OGG BigData微服务需要一些特定步骤和设置。

维基百科实时分析和利用Clusterpoint进行事务处理，Clusterpoint数据库被广泛应用于多个行业，支持24/7关键任务的网络和移动应用解决方案。从2015年1月开始，Clusterpoint提供即用型数据库即服务，帮助用户快速试用。演示文稿详细介绍了ClusterPoint在管理大数据应用中的独特功能，特别是如何利用来自维基百科的数百万篇文章数据集进行实时统计分析。