BIG DATA CONCEPT :-

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data " /> BIG DATA CONCEPT :-

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data ">


BIG DATA



BIG DATA CONCEPT :-

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2] Big data challenges include capturing datadata storagedata analysis, search, sharingtransfervisualizationquerying, updating, information privacy and data source. Big data was originally associated with three key concepts: volumevariety, and velocity. When we handle big data, we may not sample but simply observe and track what happens. Therefore, big data often includes data with sizes that exceed the capacity of traditional software to process within an acceptable time and value.

Current usage of the term big data tends to refer to the use of predictive analyticsuser behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem."[3] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[4] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searchesfintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorologygenomics,[5] connectomics, complex physics simulations, biology and environmental research.[6]

Data sets grow rapidly, to a certain extent because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[7][8] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[9] as of 2012, every day 2.5 exabytes (2.5×260 bytes) of data are generated.[10] Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. By 2025, IDC predicts there will be 163 zettabytes of data.[11] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[12]

Relational database management systems, desktop statistics[clarification needed] and software packages used to visualize data often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[13] What qualifies as being "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.

 

 

 

Characteristics

 
Shows the growth of big data's primary characteristics of volume, velocity, and variety

Big data can be described by the following characteristics:

Volume
The quantity of generated and stored data. The size of the data determines the value and potential insight, and whether it can be considered big data or not.
Variety
The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion.
Velocity
The speed at which the data is generated and processed to meet the demands and challenges that lie in the path of growth and development. Big data is often available in real-time. Compared to small data, big data are produced more continually. Two kinds of velocity related to big data are the frequency of generation and the frequency of handling, recording, and publishing.
Veracity
It is the extended definition for big data, which refers to the data quality and the data value.[28] The data quality of captured data can vary greatly, affecting the accurate analysis.

Data must be processed with advanced tools (analytics and algorithms) to reveal meaningful information. For example, to manage a factory, one must consider both visible and invisible issues with various components. Information generation algorithms must detect and address invisible issues such as machine degradation, component wear, etc. on the factory floor.

Other important characteristics of Big Data are:

Exhaustive
Whether the entire system (i.e., =all) is captured or recorded or not.
Fine-grained and uniquely lexical
Respectively, the proportion of specific data of each element per element collected and if the element and its characteristics are properly indexed or identified.
Relational
If the data collected contains commons fields that would enable a conjoining, or meta-analysis, of different data sets.
Extensional
If new fields in each element of the data collected can be added or changed easily.
Scalability
If the size of the data can expand rapidly.
Value
The utility that can be extracted from the data.
Variability
It refers to data whose value or other characteristics are shifting in relation to the context they are being generated.

 

 

 

 

 

 

 

LIST OF BIG DATA COMPANIES 

This is an alphabetical list of notable IT companies using the marketing term big data:

 



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