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  6. Application Scenarios

Application Scenarios

MapReduce can be applied to the following three scenarios:

  • Massive data analysis

Application: massive data analysis, online or offline data analysis, and business intelligence

Features: massive data, heavy computing, time-consuming data analysis, and a huge number of computers working simultaneously

Scenarios: log analysis, online or offline data analysis, scientific simulation computing, biometrics, and space-time analysis

  • Massive data storage

Application: massive data storage, query, and data warehouse

Features: massive data storage, query, and backup without data loss

Scenarios: storage of logs, files, scientific simulation data, biometric information, gene engineering data, and time-space data

  • Low-latency data stream processing

Application: real-time data analysis, continuous computing, and online or office message consumption

Features: massive data, low latency, high throughput, high reliability, easy expansion, and real-time distributed computing

Scenarios: streaming data collection, web-based tracking, data monitoring, the Internet of things (IoT) analysis, and risk control