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Weather Data Analytics Using Hadoop with Map-Reduce

机译:天气数据分析使用Hadoop与Map-Refey

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摘要

Big data describes a huge quantity of data which requires new technologies to make potential to get value from it by analysis and capturing method. In many aspects of life the weather is very critical for persons. For accurate analysis of weather, collecting, storing and processing a huge volume of weather data is needed. In agriculture sector, tourism sector and government agencies the weather forecasting has a lot of importance. A few knowledge of weather which is very helpful for human to prepare themselves for any unwanted condition of climate. In the analysis of weather conditions different some weather parameters plays an vital role such parameters are like temperature, pressure, wind speed and humidity etc. Big Data analyze the huge data-sets also it processes a large amount of data and process that data accurately. Weather analytics is the technology which shows the behavior of the environment for a particular given area. This paper presents the significant amount of data which loaded into Hadoop Distributed File System (HDFS), and it utilizes mapper and reducer function to process that data and final output will get in the form of average temperature of a particular city or location.
机译:大数据描述了大量数据,需要通过分析和捕获方法使新技术能够从中获得价值。在生活的许多方面,天气对人来说非常重要。为了准确分析天气,收集,存储和加工需要大量的天气数据。在农业部门,旅游业和政府机构的天气预报具有很大的重要性。对人类的一些天气知识非常有用,为任何不必要的气候状况做好准备。在分析天气条件下,不同的一些天气参数起到重要作用,这种参数就像温度,压力,风速和湿度等。大数据分析巨大的数据集也可以准确地处理大量数据和处理数据。天气分析是显示特定特定区域环境的行为的技术。本文介绍了加载到Hadoop分布式文件系统(HDFS)中的大量数据,它利用映射器和减速器功能来处理数据和最终输出将以特定城市或位置的平均温度的形式获得。

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