Data types

Understanding big data

Big data basically falls into two categories. Structured and unstructured data.

Structured data can be immediately identified within an electronic structure such as a relational database. For example, to retrieve the name of a city, the "city field" is accessed. XML Is Also Structured.

Unstructured Data (or unstructured information) refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well.

 

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Big data can be described by the following characteristics:

Volume
The quantity of generated data is important in this context. The size of the data determines the value and potential of the data under consideration, and whether it can actually be considered big data or not. The name ‘big data’ itself contains a term related to size, and hence the characteristic.

Variety
The type of content, and an essential fact that data analysts must know. This helps people who are associated with and analyze the data to effectively use the data to their advantage and thus uphold its importance.

Velocity
In this context, the speed at which the data is generated and processed to meet the demands and the challenges that lie in the path of growth and development.

Variability
The inconsistency the data can show at times—-which can hamper the process of handling and managing the data effectively.

Veracity
The quality of captured data, which can vary greatly. Accurate analysis depends on the veracity of source data.

Complexity
Data management can be very complex, especially when large volumes of data come from multiple sources. Data must be linked, connected, and correlated so users can grasp the information the data is supposed to convey.