International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 09 | Sep-2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 329 The ͳ7 V’s Of Big Data Arockia Panimalar.S 1 , Varnekha Shree.S 2 , Veneshia Kathrine.A 3 1 Assistant Professor, Department of BCA & M.Sc SS, Sri Krishna Arts and Science College, Tamilnadu, India 2,3 III BCA, Department of BCA & M.Sc SS, Sri Krishna Arts and Science College, Tamilnadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Big data has turned into a hot cake for many associations and can be more useful for the enterprises like banking, internet business, insurance and manufacturing and so forth to encourage their customers. Generally, at the point when the data was low in volume, it was effortlessly overseen and processed by traditional technologies. These technologies are unequipped for dealing with it as big data differs in terms of volume, velocity and value as compared to the other data. Researchers & practitioner have distinguished, characterized and explored big data in terms of its characteristics including volume, velocity, variety, value, virality, volatility, visualization, viscosity and validity. Be that as it may, these examinations have been turned out to be deficient due to the developing issues rehashed day by day. This paper has identified and defined the fourteen characteristics of big data and a new three characteristics of big data has been explored further to handle big data efficiently. Key Words: Big Data, Data, ͳͶ V’s, ͳC, ͳ7 V’s, Big Data Characteristics 1. INTRODUCTION Big data is a collection of data sets or a combination of data sets. The concept of big data has been endemic within digital communication and information science since the earliest days of computing. Big data is growing day by day because data is created by everyone and for everything from mobile devices, call centers, web servers, and social networking sites[1]. But the challenge is that it is too large, too fast and hard to handle for traditional database and existing technologies. Many organizations gather the massive amounts of data generated from high-volume transactions like call centers, sensors, web logs, and digital images. The success of their business depends on meeting big data challenges while continually improving operational efficiency. Big data is continuously including more & more data sets with high volume beyond the capability of regularly used software tools to capture, curate, handle and process data set within a tolerable elapsed time. A huge amount of data sets is created every second from every part of the world i.e. the volume of data can never be reduce but increases day by day. Nearly five years ago, personal computer storage was tens to hundreds of gigabytes. Today IDC's Digital Universe Study predicts that between 2009 and 2020 digital information data will grow by 44% from 0.8 ZB to 35 ZB. Many surveys expect that volume of data will grow by 45% in the next two years, and few said it will be doubled [1]. Thus, big data is a moving target and requires more attention to capturing it, curate it, handle it and process it. Fig. 1 shows the exponential growth of big data volume with time. Fig 1: Growth of Data 2. EVOLUTION OF ͳͶ V’s AND ͳC OF BIG DATA CHARACTERISTICS A. ͵ V’s of Big Data Big data is a new idea, and it has got numerous definitions from researchers, organizations, and individuals. In 2001, industry analyst Doung Laney (currently with Gartener), articulated the mainstream of definition of big data in terms of three V's: Volume, Velocity, and Variety[2]. Fig 2: ͵ V’s of Big Data RTAP OLAP OLTP Real Time Analytics Processing (Big Data Architecture & Technology) Online Analytical Processing (Data Warehousing) Online Transaction Processing (DBMS