BIG DATA 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. The size of big data is usually larger than terabytes and petabytes.

Variety

The type and nature of the data. The earlier technologies like RDBMSs were capable to handle structured data efficiently and effectively. However, the change in type and nature from structured to semi-structured or unstructured challenged the existing tools and technologies. The big data technologies evolved with the prime intention to capture, store, and process the semi-structured.....

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 is 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

The truthfulness or reliability of the data, which refers to the data quality and the data value. Big data must not only be large in size, but also must be reliable in order to achieve value in the analysis of it. The data quality of captured data can vary greatly, affecting an accurate analysis.

Value

The worth in information that can be achieved by the processing and analysis of large datasets. Value also can be measured by an assessment of the other qualities of big data. Value may also represent the profitability of information that is retrieved from the analysis of big data.

Variability

The characteristic of the changing formats, structure, or sources of big data. Big data can include structured, unstructured, or combinations of structured and unstructured data. Big data analysis may integrate raw data from multiple sources. The processing of raw data may also involve transformations of unstructured data to structured data.

Exhaustive

Whether the entire system (i.e., {\textstyle n}{\textstyle n}=all) is captured or recorded or not. Big data may or may not include all the available data from sources.

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 common 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 big data storage system can expand rapidly.

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