Analysts are increasingly interested in new data types, such as sentiments expressed in product reviews, unstructured textģ Data Refinery with Big Data Aspects 657 from call records and service reports, online behavior such as click streams, images and videos, and geospatial and temporal details. 1.3 Variety-It is no longer enough to collect just transactional data such as sales, inventory details, or procurement information.
Without real-time analytics to decipher these dynamic data streams, companies cannot make sense of the information in time to take meaningful action. For example, machine-generated data from sensors and web log data is being ingested in real-time by many applications. 1.2 Velocity-Data continues changing at an increasing rate of speed, making it difficult for companies to capture and analyze. According to industry analyst IDC, the digital universe created and replicated 1.8 trillion gigabytes in that is the equivalent of 57.5 billion 32GB Apple ipods. 1.1Volume-The amount of data generated by companies and their customers, competitors, and partners continues to grow exponentially. Figure 1: 3Vs model of Big Data integrity. Now much of the industry, continue to use this "3Vs" model for describing big data. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources). In a 2001 research report and related lectures, META Group defined data growth challenges and opportunities as being three-dimensional, i.e.
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Rapidly growing volumes of data from transactional systems like enterprise resource planning (ERP) software and non-transactional sources such as web logs, customer call center records, and video images are everywhere. In business publications and IT trade journals, the buzz about big data challenges is nearly deafening. Moreover, it comes from humans website data, social media, etc. It comes from our traditional systems Billing systems, ERP systems, CRM also comes from machine data from sensors, network switches. We can see the wide variety of sources of big data. The purpose of big data technology is to cost effectively manage and analyze all of the available data. What is new is the technology to process and analyze it. Big data itself is not new it has been here for a while and growing exponentially.
Another reason that big data is a hot topic in the market today and new technology that enablesĢ 656 Rajan Kumar & Sarika Gupta an organization to take advantage of the natural resources of big data. Introduction Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within time. Keywords: Big Data, cloud architecture, Hadoop, MapReduce, TeraData. Through our work, we examine the data refinery objects and application that mostly used to handle the big data aspects like Hadoop, MapReduce, cloud database, TeraData and many other platform that try to analysis the best way to handle this problem. Big data is difficult to work with using most relational database management systems and desktop statistics and visualization packages, requiring instead "massively parallel software running on tens, hundreds, or even thousands of servers. Unstructured data comes from many sources and takes many forms web logs, text files, sensor readings, user generated Content like product reviews or text messages, audio, video and still imagery and more. The majority of this data will be unstructured complex data poorly suited to management by structured storage systems like relational databases. According to IDC the amount of digital information produced in 2011 will be ten times that produced in 2006: 1,800 Exabyte s. Abstract Big data has become an extremely popular term, due to the well documentary explosion in the amount of data being stored and processed by today businesses. ISSN Volume 3, Number 7 (2013), pp International Research Publications House /ijict.htm Data Refinery with Big Data Aspects Rajan Kumar 1 and Sarika Gupta 2 1 Department of Information Technology, Dronacharya College of Engineering, Greater Noida U.P, India 2 Department of Information Technology, Dronacharya College of Engineering, Greater Noida U.P, India. 1 International Journal of Information and Computation Technology.
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