A Distributed Data Processing Platform over Meteorological Big Data Using Mapreduce

Abstract

In big data's universe, the temporal data expands exponentially, putting increasing demands on real-time analysis of big data. It is safe, too, since it will save energy. Meteorological evidence of this magnitude has drawn an abundance of interest as well the weather departments As a result, an immediate need for implementation was provided. The ability to process and store extensive meteorological data in a distributed way metrics is aimed at building an eco-friendly data mining and meteorological big data network using Map Reducel Structured to inspire originality Real-time platforms are developed in tandem with the technologies that we support.As it obtains data according to different assets, it employs data strategies accurate statistics with various sources. Map Reduce-stylemeteo manage long-term predictions is actually made easier by the distributed nature of the big data.information that is hosted on the website All we have planned is focused on improving real-time and reliable execution. The affordability and accessibility of large meteorological data on hand means it's both readily available and highly effective. It is effective in handling large data sets since it can handle large data volumes with a large memory footprint.