A Study On The Big Data

What does it mean to say “big data”? Big Data is more than just massive amounts of data stored together. It is more than just data delivered or analyzed fast. Meta Group’s Doug Laney described it as data that has volume, velocity, and variety (2001). This is the 3 V’s of Big Data and is widely used to define it. Additions to this definition include other V’s, such as veracity and value (XXX). What is volume? Volume could be 7 billion people speaking at once. It can be the data created by millions of Americans uploading photos, buying shoes online, or searching for the definition of Big Data. It is the volume of data being created by researchers at unprecedented amounts to chart the stars, to map the human genome, or to trend

There is currently a conundrum facing experts in the field of Big Data. The struggle is the ability to perform large-scale data analysis and the impracticality of using relational database processing languages to handle the information that is collected/processed. Specifically, the growth of data, the sheer volume that must be stored in databases, processed by cloud analytic and queried by applications has led to a growth in the data capacity the needs to be handled. Unfortunately, this exponential growth has exceeded the hardware and

In 2012, it was estimated, that human beings were generating around 2.5 exabytes of data every day and that number is likely even greater today (McAfee & Brynjolfsson, 2012). Twitter processes on average about 5,700 tweets per second (Twitter Inc, 2013). All of this data is stored in numerous ranging traditional database tables and spreadsheets to SMS text messages, PDF files, HTML web pages and more. While the value in capturing and analyzing this data is clear, the solution is not. Traditional data warehouse technologies were not designed for this volume, velocity and variety of data, which is collectively referred to as big data.

Big data refers to the large amount of data that is impossible to handle by using traditional or conventional methods such as relational databases or it is a technique that is required to handle the large amount of data that is generated with advancements in technology and increase in population. Big data helps to store, retrieve and modify these large data sets. For example, with the advent of smart technology there is rapid increase in use of mobile phones due to which large amount of data is generated every second, so it is impossible to handle by using traditional methods hence to overcome this problem big data concepts were introduced.

Big Data has taken the business world by storm. By 2020, it is expected that the amount of digital information in existence will have grown from 3.2 zettabytes in 2014 to 40 zettabytes. Companies are doing all they can to capture this digital information and turn it into actionable insights. Currently, the total amount of data being captured and stored by industry is doubling every 1.2 years. Therefore, companies must find increasingly efficient solutions to store and analyze this incredible amount of data.

Additionally, social networking website Facebook, stores approximately 40 billion photos in total. (“Data, data everywhere”, 2010) Besides enormous data that generated from daily operational company transactions and social networks, the price drop of the data storage is also a strong factor triggering the fever of “Big Data”. For example, Google Drive - a cloud based data storage service – had a price drop of approximately 80% from March 2014. This price drop is considered a marketing approach to attract more computer users to adopt Google’s cloud service, which provides a more convenient and efficient way to access and store daily-used files. Although emerge of enormous data provides us opportunities to conduct further investigation and benchmarking, valuable information are not fully extracted and the potential power of using “Big Data” is undermined. In order to achieve thoroughly extraction of useful information from databases, many professionals in the academic field devoted into the study of data analysis and identified two of the most important drawbacks of traditional data analysis, which lacks of predictability and is less flexible in scalability.

Big data is an emerging term which has been noticed since it influences our daily life gradually. The big data is a large concept that is vague because different people look at big data phenomenon from a different perspectives, it is not easy to make a precise definition (Moorthy, et. al., 2015). The definition of big data is a matter of debate, however, a typical reference is to the collection, management, and analysis of massive amounts of data (McNeely & Hahm, 2014).According to George et. al. (2014), big data include Internet clicks, mobile transactions, user-generated content, and social media and content from sensor networks or business transactions, such as sales queries and purchase transactions. These procedures are significant to

Big data refers to the growing data a company experiences but this data has to be managed and processed to convert it into useful information. For example, a company XYZ just launched an e-commerce website and its receiving a

The “Big Data” becomes a common word now in Information Technology and Business world. These two simple English words created history and meant a ton in Global market. In past the data refers to traditional data and data volumes either in single or multiple terabyte ranges. But today it’s beyond traditional data and includes real time transactional data which is a key to the business systems.

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

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis or in other words big data is a collection of data sets which is very large in size as well as complex.

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.

To address the question of how and what techniques has been used to manages this big amount of data or in the field of Big Data, I review some research papers and review articles in the field of Big Data. This paper provides the synthesis of those papers which I found relevant to this field. This paper will focus on the following things:

Big Data is the data that surpasses the processing limit of the customary database frameworks. The data is huge, moves with more speed and does not fit the structure of the conventional database frameworks.

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}.