Impact of Big Data on the Networks of Ray business Essays

Impact of Big Data on the Networks of Ray business technologies and Cognizant technological solution Student Name: Institutional Affiliation: Course: Date: Abstract Big data is obviously the biggest buzz phrases in information technology today. Together with cloud computing and virtualization, big data is an invention that shall pressure data centers to transform significantly and grow within the coming years ( McAfee, 2012) . Just as virtualization, the infrastructure of big data is unique and could lead to architectural turmoil in the manner storage, systems and software are managed and connected. As opposed to solutions on business analytics, the real-time efficiency of emerging big data solutions could offer mission critical business insights that can reshape and promote decision making CITATION ava10 \l 1033 (avanade, 2010) . Due to the infrastructure architecture and distribution demands analysis of the various network implication. Thi s paper investigates the impact of Big data on networks of two organizations namely Cognizant technology solutions corporation and Ray business technologies. Accordi ng to estimates by IDC, the digital universe size in 2011 was 1.8 trillion gigabytes CITATION Luc12 \l 1033 (Lucinda, 2012) . With information currently exceeding Moore's law, Cognizant technology solutions and Ray business technologies will be forced to deal with 50 times of more data come 2020 while increasing the workforce by only 1.5 percent. With this imminent challenge in cognizance, the adoption of Big data models into the organizations existing infrastructure is a vital consideration when looking at the impact posed by Big data. Introduction Big data is obviously the next big thing. Every publication that focuses on it promises a great deal of insight CITATION Jam11 \l 1033 (James, 2011) . A network is however, critical in gathering and distributing data to various processing centers CITATION Vil13 \l 1033 (Vilas, 2013) . Most applications on Big data demand real-time communications. Organizations therefore need to plan their networks early before the issue arrives. Most organizations like the aspect of unravelling actionable, unexpected business insights, through the use of sophisticated analytics to vast amounts of unstructured, structured data. Big data definitely comes with a huge price. Specifically, networks could get slammed by such technology. Issues around the impact of big data on networks are a subject of concern to technology experts. Regardless of the fact that tools on big data are undeveloped and persons with skills on its use are very few, big data seems to burden information technology infrastructure as well as its operations. Essentially, this companies cannot put a stop to Big data, their only alternative is to prepare for it. Unfortunately, big data comes with huge networking demands. Nevertheless, the intelligence or data from big data is of any value if they can be adequately harnessed or get into various destinations on time. Network requirements The huge impact of big data on networking on these two companies becomes more imminent as they try to grow and expand their technological adoption. The two companies as advanced big data users due to their existence and sizes. It is obvious that their network traffic would exponentially increase from collection of big data as well as significant increase from backups of big data. Considering this network traf fic surge triggered by big data, these companies would have to prepare on frameworks to mitigate on such conditions. Both Cognizant technological solutions and Ray business technologies would be compelled by Big data to tweak their regular operational procedures. Consequently, network managers of both these companies have to effectively adjust their performance troubleshooting and monitoring processes and tools due to the busty nature and increased volume of network traffic implications of big data CITATION IBM14 \l 1033 (IBM, 2014) . As a result of the numerous traffic patterns and sources by Big Data, enterprise networks must handle the ideology of traffic shift from the common server to client model to a server to server traffic model within the fabric of a data center ( Chen, 2012) . This nature of horizontal flow factors in connections between servers and demands intelligent storage systems to be upgraded. Big data brings up its own requirements for network infrastructure and must integrate critical functions like collection, storage, creation and data analysis. These specific processing requirements are