Big Data: For further understanding of this concept we have to have a look at the classification of the 4 dimensions corresponding to Big Data, respectively as Volume, Velocity, Variety and Veracity.Big Data: For further understanding of this concept we have to have a look at the classification of the 4 dimensions corresponding to Big Data, respectively as Volume, Velocity, Variety and Veracity.
The Big data is a term used for the massive data set which very difficult to process, store, search, update and delete using traditional database and software techniques. These dataset includes both structured and unstructured data. This article is discussing about the Big data.
An Example of Big Data is the data of millions of people around the world collected from different source such as web, sales, customer contact center, social media websites, mobile data, chat data, communication data etc.. These data might be petabytes (1,024 terabytes) or exabytes (1,024 petabytes) in size. These data might be loosely structured and most of the cases it is incomplete.
By many Big Data represents the last envisaged frontier of present development and computing research that is expected to change the way we think, work and live in many ways as overseen by researchers and analysts. For the last few decades we have seen enormous evolution in tech gadgets, tech standards and corresponding applications. Now your weather forecast to news to class notes to entertainment visuals to emails and live projects at workplace, all are accessible from your handheld device just throughout the day without depending on the location and specific devices. Suddenly computing and information network that it shares has become accessible at every point of time through evolved range smaller smart hand held devices. If this non location specific and non device specific sharing and accessibility of information between multiple ranges of devices represents the present IT scene, then Big Data is certainly going to be the next big leap. Let us now have a look at the definition of this term before proceeding further to look at the four dimensions of Big Data and its varying influences on future business analytics.
Big Data in simple yet authentic definition denotes large or bigger body of data that is still beyond the ability of commonly used software tools to store, manage and process. As per the statistics provided by IBM, one of the forerunner companies in Big Data research every day we create more than 2.5 quintillion bytes of data and to understand the volume better the company says 90% of the data in the world today are created just in the preceding two years only. Just imagine how far the input of information and data has moved forward. Every word you write, say or every image you provide, every movement you capture and store on your device or share in the network is part of this hilarious and continually heaping over body of data. Now the source of this Big Data can be innumerable, from weather information to input in social media to a shared video to a transaction record to voice recorded and shared to digital images, almost anything that eats some bytes space on the computing network.
For further understanding of this concept we have to have a look at the classification of the 4 dimensions corresponding to Big Data, respectively as Volume, Velocity, Variety and Veracity.
Volume: The tons of information in any of the billions of network poured in a single day can make easily understand the ever growing volume of data. Further research on Big Data is focused on making this volume available for various kinds of analysis in enterprises or business organizations. For instance, billions transaction data in a retail chain can be subject to analyze buying trend or consumer's buying frequency for select products or for example trillions of fuel bills can be subject to analysis for next vehicle fuel policy.
Velocity: Big data has much more bigger implications in time sensitive business processes than others. It is always a hurried process to analyze to scrutinize maximum volume of data for a potential business objective like catching a fraud in transactions or locating the exact reason of why clients of a particular business process are not coming back. Only faster scrutinizing capability that can handle large volume of data in real time can translate into business benefits. Faster processing of time sensitive data is to give you an edge in fault finding or finding the hidden loop in the process, that is exactly one of the demands of Big Data that is increasingly becoming crucial.
Variety: The widest possible variety of data types is one aspect corresponding to Big Data analysis that is going to pave the way for numerous benefits for big to small, all sorts of organizations. Big Data comprises any type of data, both structured and non-structured. It can be audio visual, graphic representation, spreadsheets and log files, 3D images to simple text to click links or simply anything. When these multifarious types of data are analyzed together they may provide great range of insights for particular researches. For instance tons and tons of text messages over a football match can be analyzed in contradiction to measurably small number of actual spectators which may indicate a necessity for changing marketing and publicity tactics for the event managers and organizers of the match.
Veracity: Accuracy or trustworthiness of information is one aspect that challenges the use of data in business analysis or trend analytics. While many business managers and top decision makers are still skeptics about the accuracy and corresponding outcome of business analysis based on various sources of data, when this body of data grows enormously bigger to contain various contradictory trends and aspects it can as well be a good basis for determining the accuracy. As the volume grows bigger in Big Data analysis, the efforts motivated by partial observation becomes futile and thus exceptionally Big Data reserves when handled properly can render more accurate observations.
The Hadoop cluster is used to store and search search large set of data.