• Sudhir Allam

This paper looks at how multiple big data platforms can be very important in achieving the best outcome when used in rapid response operations. Big data analytics and technological innovation place a strain on traditional data governance processes, creating a slew of complex issues inside corporations. However, a governed data lake demonstrates how this can be accomplished [1]. The most difficult aspect of integrating different systems is figuring out how to adjust to their unique programming capabilities to complete tasks as quickly as possible to achieve the best results. This paper introduces the latest big data analytics platforms for a rapid response which integrates large-scale data frameworks like RHadoop and SparkR to build high-performance, multi-platform, Big Data analysis for rapid data collection and R-programming analysis [1]. The goal is to improve work scheduling optimization using the big data analytics platforms and to apply the customized device selection based on ascertaining capabilities to significantly boost system performance. Furthermore, rather than running Java or Scala programs, users will issue R commands to conduct data extraction and analytics in the presented platforms [1]. As a consequence, while the configured data analytics platform selection will greatly minimize data extraction and analysis period, and as per the output index measured for different approaches, planned optimization almost certainly improves device reliability considerably.

Big data, Analytics, Hadoop, Spark, RHadoop

"Significance of developing Multiple Big Data Analytics Platforms with Rapid Response", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.2, Issue 3, page no.777-783, March-2015, Available :http://www.jetir.org/papers/JETIR1701523.pdf

"Significance of developing Multiple Big Data Analytics Platforms with Rapid Response", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.2, Issue 3, page no. pp777-783, March-2015, Available at : http://www.jetir.org/papers/JETIR1701523.pdf