The Importance Of The Unstructured Information On The Data And Processing Essay

Volume: (SAS, 1) The amount of data, and more importantly the ability to cheaply store large amounts have data have contributed to a better understanding of many fields. There are an ever increasing amount of data as social media continues to saturate the online experience. Social media sites such as Facebook and Twitter can generate Petabytes of data that can then be distilled. There is also the emergence of the IoT

“datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze,” the McKinsey researchers acknowledged that “this definition is intentionally subjective and incorporates a moving definition of how big a dataset needs to be in order to be considered 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

What is the definition of big data? What the difference between big data and data? Figuring out these two questions can help us to comprehend big data in depth. Big data has been defined as the data that is too big to be analyzed by traditional methods. Generally speaking, there are five characteristics for big data: data volume, data variety, data velocity, data variability, and data complexity, which were also known as the five dimensions for big data.3 Data volume means that the measurement of the units of data storage. Data variety indicates the diverse forms of data. Data velocity has been defined as the velocity of data producing and processing. Data variability means that the data flow could be inconsistent. Data complexity indicates that the data is hard to analyze. Also, the five dimensions of big data can be seen as the differences between big data and data. Figure 1.13 shows the dimension diagram for big data.

“Why Big-Data Is a Big Deal”. Big-data is a logo used to describe a massive volume of both structured (is information already managed by the organization in relational databases ) and unstructured data (is information that is unorganized and does not fall into a pre-determined model) that is so large it is difficult to process using traditional database and software techniques. In most companies, the volume of data is too big or it moves too fast or it exceeds current processing capacity. Despite these problems, big-data has the potential to help companies improve operations and make faster, more intelligent decisions.

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.

Volume: The term big data itself tells it is related to size. Big data requires processing of high volumes of unstructured data such as data from twitter, network traffic etc. The volume of data varies from one organization to other. Velocity: The term velocity refers to how fast the data is generated and processed to meet the demands and the challenges in the path of growth and development. Reacting quickly enough to deal with data velocity is a challenge for most organizations. Variety: Data today comes in various formats, types, structured, unstructured. For example, the data may

We used the expression " A huge database" when data is accumulated and increased rapidly, The a huge is a relative matter, but in general, a huge database is a data set that exceeds the size or capacity of conventional database tools to fetch, store, manage and analyze that data. Which means that the challenge is manifold when we try to analyze and extract a meaningful value from a huge unstructured database

Big data refers to the collection and subsequent analysis of any significantly large collection of data that may contain hidden insights or intelligence (user data, sensor data, machine data). when analyzed properly, big data can

Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Big Data focus more on finding new correlations of massive of data sets not only the causation. Big data as a term maybe new, but many IT and business analysts have worked with huge amount of data in various industries for years.

Defined before as information not so straightforward to interpret, instead of the easy to read data that is the structured method. Unstructured data is generated in media like email messages, PowerPoint presentations, Word documents and also media like JPEG images, MP3 audio files [9]. These results factor into whatever type of data analytics the company is after.

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.

The term Big Data is to a large extent vague and amorphous. Information technology professionals look at Big Data as large data sets that require supercomputers to collate, process and analyse to draw meaningful conclusions. A phenomenon defined by the rapid acceleration in the expanding volume of high velocity, complex, and diverse types of data. The new character added in this definition is

There can also be a fourth V (Vossen, 2014) which adds veracity to volume, variety and velocity to give 4V’s. Veracity can be viewed to be the messiness of the data (thinking of things such as Tweets with hash tags, abbreviations, typos, and

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