The Realm Of Big Data

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

This article reviews the application of big data and big data analysis at Facebook. As explained above, social networking sites such as Facebook generate terabytes of information on a daily basis and thus have the opportunity of generating massive amounts of knowledge from these data. The article has been divided into five chapters including the introduction, a brief description into Facebook, the big data strategies at Facebook, the big data technologies at the company to support strategic operations and the implementation of the big data strategies at the company. The paper then concludes with a brief conclusion briefing on the findings and the results of the discussion.

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.

With the growth of technology, there is a substantial growth of data by volume, variety and velocity satisfying the criteria of Big Data . Volume of data already exceeded 100 EB at the end of 1990s, reached 1.8 ZB at 2011, and we have already entered in the age of ZB. By 2020, it was forecasted that the volume of data will be 50 times bigger than the one at 2011 . Big data is used to describe the huge data sets (terabyte to Exabyte) and Big Data analytics are the techniques applied on them.

Businesses using data is not a new concept; however, the role of data within industries has increased dramatically over the years to the point that it is essential for a business to understand how to handle data in order to continue operations. In today’s bustling digital age, professionals credit a certain type of data called “big data” with helping businesses gain insight on consumers. Big data is created whenever you travel to your favorite restaurant, make a particular move in a video game, swipe your card to purchase your favorite pair of Crocs, or tell your Facebook friends what you had for breakfast. It is data that is too large to be captured and processed by standard business

Imagine being in a room with 10 people talking to you. Would you be able to understand the conversations? It would be hard to concentrate on any one person’s conversation, but you could probably do it. Now, imagine being at the Superdome with 70,000 people talking to you. Would you be able to understand any of the conversations? You probably could not understand any one person for a length of time, but again you might pick out some of the words. Well, what if the scenario was 7 billion people speaking 6 thousand languages at an auctioneer’s pace on the topic of their personal family lineage; would you be able to create someone’s whole family tree? This is the type of scenario that represents the different facets of Big Data.

Big Data can analyze a customer’s purchases, profile information, and make an accurate prediction on what his/her interests will be. Big Data has the potential to be the phenomenal tool of the future. It can accurately predict what a customer wants and show advertisements that he or she is actually interested in; It can be installed in smart cars, where an automatic distress call is sent when an unlucky individual is in a car accident; It can be uploaded to devices that closely monitor people’s health and report any irregularities to their doctor.4

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.

Firstly, the main problem is deciding which data should be selected. The data, explaining customers’ desires and needs, is important to be collected while most of the enterprises are confusing about what data they should concentrate on. A recent Gartner report (2014) stresses that 64% of firms raced to plan or launch a Big Data project, though they did not have enough professional knowledge yet. To understand what customers need through Big Data possibly turns into the core of companies’ target. The large data volumes and different varieties of data lead to data complexity.

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.

Data is the backbone of business today and has always played a critical role in business. Today in the era of “Big Data” and Digital Business, data has become the primary driver of decision making, growth and innovation. The big data today is radically different from the data of yesterday. The Big Data age has brought with it a tremendous increase in the amount of data and types of data available to businesses. New data is produced every day, generated by social networking sites, mobile phones, location, third party, business transactions, etc. We are in an era which is characterized by the 5 V’s of Big Data: Volume, Velocity, Variety, Veracity and Value. The Big Data opportunities are enormous, as are its challenges. In this context it is especially important to understand the Opportunities and Problems that business faces to extract value from Big Data Analytics.

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

Background: Big Data has a background of data aggregation and metadata. It is the basis for future of business analytics.

Big data provides suitable architecture for current trends and also to meet the requirements in changing business and its functions.

Big data and Analytics have become very prominent areas of study in recent years. Company revenues exceeding more than one million are found to use some or the other form of business analytics. The techniques, technologies, systems, practices, methodologies and applications help analyze the data in the organizations to make critical decisions.