Big Dat The Good And The Ugly

Big Data is an expansive phrase for data sets so called big, large or complex that they are very difficult to process using traditional data processing applications. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. In common usage, the term big data has largely come to refer simply to the use of predictive analytics. Big data is a set of techniques and technologies that need or require new forms of integration to expose large invisible values from large datasets that are diverse, complex, and of a massive scale. When big data is effectively and efficiently captured, processed, and analyzed, companies

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.

The industry is inundated with articles on big data. Big data news is no longer confined to the technical web pages. You can read about big data in the mainstream business publications such as Forbes and The Economist. Each week the media reports on breakthroughs, startups, funding and customer use cases. No matter your source for information on big data, one thing they all have in common is that the amount of information an organization will manage is only going to increase; this is what’s driving the ‘big data’ movement.

Today, the data consumption rate is tremendously expanding, the amount of data generated and stored is nearly imperceivable and highly growing. Big data that is nothing but a large volume of unstructured or structured data that runs in and out in to a business on daily basis. This big data is analyzed in order to achieve prominent business growth and improved business strategies [1]. Every year there is at least 40% increase in the amount of data growth on global level, leading to which companies have started adopting new data analytic techniques and tools and also have stepped ahead moving their data towards the cloud for their big data analytic requirements and for better analysis.[3][2] In big data analysis it is not the amount of data that is essential but how efficiently we handle, process and analyze it is the key factor. Big data analysis doesn’t revolve around how much data we occupy, it deals with how well you make use

Every year the data transmitted over the internet is growing exponentially. By the end of 2016, Cisco estimates that the annual global data traffic will reach 6.6 zettabytes. The challenge will be not only to “speed up” the internet connections, but also to develop software systems that will be able to handle large data requests in optimal time. For the definition of the Big Data, there are various different

According to a report from The International Business Machines Corporation, known as IBM, 90% of the data in the world has been generated in the last two years. Frank J. Ohlhorst (2013) explains how the concept of collecting data for use in business is not new, but the scale of data that has been collected recently is so large that it has been termed Big Data (p. 1). Company executives who choose to ignore Big Data are denying their companies an advantage over their competitors. Big Data analysis is fundamental for all fields of work; it provides an insight to large amounts of data that will answer questions and make discoveries to improve efficiency in all areas of the world.

Big data is an extensive collection of structured and unstructured data. It is a modern day technology which is applied to store, manage and analyze data that are not possible to manage, store and analyze by using the commonly used software or tools. Since all of our daily tasks are overtaken by the modern technologies and all the businesses and organizations are using internet system to operate, the production of data has increased significantly in past

Volume is the high amount of data that comes in.Variety is the data is a combination of structured, Un-Structured, Quasi-structured and semi-structured data. Velocity is the need to stream in the data in real time to analyze them and provide best reports. Veracity is an additional challenge which is dealing with authenticity of the data in one way to the other (Langit, 2015).

In modern times, the amount of data being stored is terrifically large. Companies must deal with such abundance of data on a daily basis in both storing and analyzing as fast as they can. One such company that not only store data is Google, they also analyze data from each user using their product. The platform used by google for this database management called BigQuery, which runs in the cloud and provides real time information. In this survey, the inner working of BigQuery is glossed over to show how this platform manages to do the job it is supposed to accomplish.

Big data is not a hype, but it is the future. The big data industry continues to advance, and big data service providers are making it easier for companies to work with big data in driving their businesses. Progressively, greater volumes and varieties of data will be incorporated with more business processes to support better decision making and greater insight. Moreover,

Data is a powerful weapon as well as a resource. Having data does not make you powerful but what you do with it makes all the difference. Companies like Amazon, eBay and Netflix are already using data to predict user behavior and utilizing that to increase their revenue. But processing data in real time is not an easy task. The data today has great volume, is veracious in nature and is increasing at an enormous rate and hence has been given the term Big Data. There is a constant research going on to find a solution to process such huge amount of data in real time.

Data has always been analyzed within companies and used to help benefit the future of businesses. However, the evolution of how the data stored, combined, analyzed and used to predict the pattern and tendencies of consumers has evolved as technology has seen numerous advancements throughout the past century. In the 1900s databases began as “computer hard disks” and in 1965, after many other discoveries including voice recognition, “the US Government plans the world’s first data center to store 742 million tax returns and 175 million sets of fingerprints on magnetic tape.” The evolution of data and how it evolved into forming large databases continues in 1991 when the internet began to pop up and “digital storage became more cost effective than paper. And with the constant increase of the data supplied digitally, Hadoop was created in 2005 and from that point forward there was “14.7 Exabytes of new information are produced this year" and this number is rapidly increasing with a lot of mobile devices the people in our society have today (Marr). The evolution of the internet and then the expansion of the number of mobile devices society has access to today led data to evolve and companies now need large central Database management systems in order to run an efficient and a successful business.

In the last few years the global marketplace has seen exponential growth in data volume. Every day people create unstructured large datasets of different types such as GPS coordinates, payment transactions, web data, e-mails or smart meter values that are termed as "big data" \cite{nasscom}.

Abstract— Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve.

In today’s world, the amount of unstructured data collected is humungous. This unstructured data is of no use if it is not properly processed, analyzed and evaluated. Using this data for the betterment of mankind is what most of the largest companies like Google, Facebook, Amazon, Netflix and much more are targeting. Big data is a term for datasets which are so large and complex that traditional database systems such as MS SQL, MySQL, etc., are incapable of handling them. It is not the amount of data that is important, but what organizations do with data that matters the most. Data can be mapped to useful information which can be further utilized for analyzing and drawing insights that lead to better management practices and strategic