A Big Data Analysis: Weather Forecasting Data Analaysis With Fixed Width Clustering Algorithm - Patil, A; Palve, A (International Journal of Computer Sciences and Engineering, 2025)
In today’s digital world remote senses daily generate large amount of real time data know as Big-Data, wherever insight data encompasses a potential significance if collected and aggregative effectively. There is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a main computational disputes, such as to analyze, aggregate, & store, where data are remotely gathered. Keeping in view the above mentioned factors, there's a requirement for planning a system design that welcomes each real-time, additionally as offline processing. In this paper we propose efficient and scalable solution to analyze pent bytes of data across an extremely wide increasing wealth of weather variables. In this research we are working on data analysis using Apache Hadoop and Java . Extensive experiments are carried out to find out the best tools among Distributed computing using Pig and Hive Queries. The proposed architecture has the potential of dividing, load balancing, & parallel processing of only utile data. Thus, it results in effectively analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Fixed width clustering algorithm is used to improve the accuracy of results.