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What Is Big Data?

Big data refers to different data sets that are too large or too complex to be processed by traditional data processing application software.

Also, data with many fields (rows) provides greater statistical power, while more complex data can lead to a higher false detection rate.

Also, big data analytics challenges include data collection, storage, analysis, sharing, transferring, displaying, querying, updating, privacy, and data source.

Therefore, big data was initially associated with three key concepts: volume, variety, and velocity.

Hence, Big data analysis also presents sampling challenges and thus has only allowed for observations and samples to date.

Therefore, Big Data often includes data of a size that exceeds the ability of traditional software to process in a reasonable amount of time and value.

Big Data Characteristics

The following characteristics can describe big data:


The amount of data generated and stored.

Also, the size of the data determines the potential value and insights and whether or not it can be considered big data.


Firstly it shows the type and nature of the data.

Also, previous technologies, such as RDBMS, could process structured data efficiently and effectively.


Firstly, the speed of data generates and processes to meet the needs and challenges.

Also, it lies in the path of growth and development. Big data is often available in real-time.


Firstly, the integrity or reliability of the data relates to the quality and value of the data.


Therefore, informational value can be from processing and analyzing large amounts of data.

Also, value can be measured by evaluating the other qualities of big data.


Firstly, the characteristic of changing formats, structures or sources of big data.

Also, big data can include structured, unstructured, or a combination of structured and unstructured data.

Therefore, raw data processing may also include transformations from unstructured to structured data.


Whether or not the entire system is captured or logged.

Therefore, big data may or may not include all available data from sources.

Fine-grained and uniquely lexical

It gives the proportion of item-specific data per item collected and whether it and its characteristics are properly indexed or identified.


When the data collected contains standard fields, that will allow the fusion or meta-analysis of different data sets.


When new fields can be easily added or changed on each collected data item.


It shows whether the size of the big data storage system can be expanded rapidly.

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