Information Technology And Its Rapid Growth Of Emerging Application

Big data analytics is the process of analyzing large data to find useful information such as improving efficiency of business, market trends, customer’s preferences, information of competitors, and other useful business information. According to the IT Glossary, “Big Data is high-volume, high-velocity, and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.” In other words, it is an abundant array of information used to acquire insights and make business decisions.

Big Data is not just simply come from itself it is been recorded and generated by some data generating source. For instance, if we have our ability to feel and realize the arena around us, from our own heart rate and getting the data of toxins that are present in the air we leave this will produce more than one million terabytes of unparalleled data for every day. Furthermore, scientific experiments will also produce petabytes of data today.

One of the most fundamental questions concerning Big Data is the question of how big the data should be in order to be treated as Big Data (Fingar, 2011). Probably the best answer is that it depends. The basic answer is whenever the system needs to span horizontally in order to handle the data, that probably there is a need to shift towards Big Data solutions (Fingar, 2011). Also, the size of the dataset is itself a significant part of the problem. Typically in modern discussions experts are operating on terabytes and zettabytes.

The Problem now the present world facing is data. Web and the Enterprise related data is developing at a speed and difficult to retrieve information and most of the information is unstructured. And the problem with data is we cannot move the data to processing unit so people done different statistical models to move processing unit in to data instead data to processing unit. They done lt of Research and introduced map reduction to reduce data. Here data is used in pipelined and processing unit acts as master and slave approach. we require an approach to deal with this information or rather create imperative data. So what amount is Big Data truly

In a fast paced, business ordinated technological world the overall welfare of a company is tied to the success or failure to make the tough decisions. On one instance a company’s CEO might be able to make the choices based on experience, advice, or simple gut instinct. However, this is not the only skill one needs. There is a great deal of information to be found in being able to see investments in data and analytics. These decisions are based off of big data. Big data is a catch-phrase, used to describe a massive volume of both structured and unstructured data that is too large to process using traditional database and software techniques. The volume of data is in most cases is too big, moves too fast or it exceed the processing capacity the company has. Despite these potential drawbacks, big data contains the potential to help companies by improving operations and making faster, more intelligent decisions. This can be broken into three key parts, knowledge, data, and information.

According to (Gartner Inc, 2013) Big Data is high-volume: the amount of data is extremely large, high-velocity: the data can be gathered quickly in real or next to real time and/or high-variety: the data can take many forms both structured and unstructured. This data then requires suitable processing to give added advantages to

Big data is the present most-liked theme of today 's technology. These research goes through all description of techniques and technologies of extracting of the data, storing of data, distribution of data, analyzing of data, managing of data with high velocity and from the structured data and helps in the handling of the extreme data. Big data has the presentation the capacity to improve predictions, saving money and enhancing the decision making process in the fields of the traffic control, weather forecasting, disaster prevention, fraud control, business transaction, education system, health and the national security.

The term “Big Data” refers to the massive amounts of digital information companies and governments collect about human beings and environment. The amount of data produced or gathered is increasing at a faster rate and is like to double after every two years i.e., from 2500 exabytes in 2012 to 40,000 exabytes in 2020 . Security and privacy issues are describes by the 3 V’s of big data- volume, variety, and velocity.

Big Data is the extremely large datasets that their sizes are beyond the ability of capturing,

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 defined by three characteristics: volume, velocity, and variety. Volume is the size of the data stored, and is measured in terabytes, petabytes, or exabytes. Velocity is the rate at which data is generated. Variety refers to the types of data, such as structured, semi-structured, or non-structured [Mahmood13]. Structured data is data that typically resides in a

Big data is not just about having large amounts of data, but it also refers to the complexity of data sets fetched from multiple sources, where traditional data processing methods cannot be sufficient to process the data, and thus requires advanced computing tools and technologies which can be acquired by using distributed processing and cloud technology. In general, Big data excels in three areas: Volume, Variety and Velocity. Volume and Variety is when you are dealing with a huge quantity of data, in all types of forms and all types of sources. Velocity is about additional capabilities offered by distributed processing, which drastically accelerates the computing speed. But, when it comes to logistics, Big data is more than just 3Vs. It goes beyond and presents real-world use cases, revealing what is happening now and what could possibly happen in the future. Big data is often unstructured, so you definitely need advance mechanism for interpreting the data. The major challenges faced are data analysing, capturing, curating, storing, searching, querying, updating, sharing, transferring, visualising and security. As of 2012, the size of Big data ranges from a few terabytes to dozens of petabytes.

Firstly it repersents the volume attribute lot of data sizes which are explained numerically. For example, users of YouTube uplosds new vedios every minute of a day in 48 hours. Second is taht the velocity is other attribute of big data. When data is sensetive for time the velocity attribute exists. Further data can be structured or unstructured then it brings the 3rd attribute named as velocity. Moreover, validity is another factor for big data. Data can be logical is defination of validity. Further more, is veracity which means conformity of facts and accuracy. Lastly, visibility is also a big data charactrstic. When data is seprate from other resources it can be seen.

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.

Two extra terms that can run with Big Data are Variability and Complexity. Fluctuation, notwithstanding the expanding speeds and assortments of