Big Data And Analytics Analysis

The analysis of big data is the process of organizing, collection, analyze and examining the large volume of data to find patterns, market trends and useful information. This analysis helps organizations to better understanding about the information within data, and helps analyst to make better

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

1) As big data is emphasized by more and more people, and the techniques and analytical

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.

Big data is anything which is too large for traditional databases to handle. They range from Terabytes of data to petabytes of data. Big data is generated from various sources, such as social media networks, oil wells, mobile phone conversations, weather data etc.

Big data is not as new as many people believe it to be. It is actually a concept that has been around for almost a century. It is just the “same old data marketers have always used, and it’s not all that big, and it’s something we should be embracing, not fearing” (Arthur). In 1944, Fremont Rider “predicted that the amount of data in the world would increase exponentially” (Hopp). Rider was right on target with his prediction seventy years ago. Data has grown much greater than he probably could have ever imagined back then.

When you hear the word “big data” what it is that first comes to your mind, a straight forward answer would be huge amount of data ranging between tens or hundreds of peta bytes to few zeta bytes of data. In a way, Big Data is not just about the amount or volume of data but one way it is about deriving business value from a range of new and emerging data sources, including social media data, location data generated by smart phones and other roaming devices, public information available online and data from sensors embedded in cars, buildings and other objects — and much more besides. Another way to define big data would be a 4V model wherein Vs stand for Volume, Velocity, Variety and Veracity. The next question that pops in would be what led to this huge pile of data, well certainly days or months or years is

Big Data is a term for very large amounts of formal and informal information that can be analyzed to find trends and patterns. The information can be about anything, but it needs to be processed in a way that will give it value and relevance. It can come in multiple formats and from different sources such as large databases, electronic records, social media, mobile phones, apps, wearable devices such as pedometers, and others. Different data sets are combined and contrasted in different ways to give perspectives and insights about a topic. It can be used in a seemly endless number of ways and people are discovering new ways to use it all the time, some of them entertaining. The largest areas of use include those relating to consumer behavior and choices, business procedures, healthcare, science and research, and law enforcement. People are also discovering its use in their personal lives as well for things like buying a home, dating, fitness routines and travel.

Big data came about the scene during the first decade of the 21st century. Those online and startup firms to survive the dot com bubble burst were the very first to embrace the technology. Companies like Google and eBay were a couple of the first firms to implement big data. They never had legacy systems to integrate big data into because they began with big data. “Big data could stand alone, big data analytics could be the only focus of analytics, and big data technology architectures could be the only architecture” (Davenport, and Dyche, “Big Data in Big Companies”). Even though big data was implemented from the get-go with firms such as Google, LinkedIn, and eBay, etc., it began to slowly descend into other companies that saw what

Increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have allowed the building of repositories to store this data. Improved ETL and enterprise application integration tools have increased the timely collecting of data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. As of 2010[update], business intelligence has become the art of sieving through large amounts of data, extracting useful information and turning that information into actionable knowledge.[citation needed]

The world is changing with respect to the growth in big data and to the way in which it is used. Growth in big data brings with it many challenges, but it also presents new opportunities. Figure 1, helps understand some of the big data related activities that are taking place in the world with respect to volume of data that is being consumed by these activities over the next 5 years.

large amounts of data everyday . Big data is the technology which analyses large sets of data

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 amount of data generated. The speed of data generated is velocity, Variety refers the format or types of data generated. Veracity is considered as uncertainty of data.

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.