Climate Data Analysis using Hadoop |
Author(s): |
Gupta Abhishek , Alamuri Ratnamala Institute of Engineering and Tecnology; Sharma Rahul, Alamuri Ratnamala Institute of Engineering and tecnology; Rokade Gaurav, Alamuri Ratnamala Institute of Engineering and Tecnology |
Keywords: |
Distributed file system, Hadoop, Distributed systems, MapReduce, Weather prediction |
Abstract |
We focus on building a platform that is extremely flexible and scalable to be able to analyze huge amount of data which is received from different weather sources. Here in this project we are working on data analysis using Hadoop. Then displaying the data to users that are interested in weather reports and its daily uses. We analyze the data that is picked up from different locations based on user preference that is sorted using mapreduce and then process the data to give the further information about the climatic changes in that particular area. Big Data is that data that cannot be processed or used with our normal database systems. The data is too big, moves too fast and is unstructured and needs a lot of processing. To gain value from this data we need an alternative way to process it, some examples of large organization that generate such huge amounts of data are Twitter, Facebook, and Weather Stations etc. Thus in our project we are dealing with huge amount of weather data which is unstructured. Our paper focuses on shifting process from single node data processing to Hadoop distributed file system for faster processing. Weather forecasting is always a big challenge for the meteorologists to project the state of the atmosphere at some future time and the weather conditions that may be expected. Prediction of weather is important for individual as well as organization, An accurate weather forecast is needed in Aviation Industry, A farmer who needs to plan its further cultivation, People living near costal areas etc. |
Other Details |
Paper ID: IJSRDV8I20463 Published in: Volume : 8, Issue : 2 Publication Date: 01/05/2020 Page(s): 390-392 |
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