Urgench state University Physics and mathematics faculty Speciality: «5111018-Professional education: Informatics and Information technologies» Group and student name: 181-inf Babaev Saidmukhammadjon

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.
In other words, they are trying to tell us that there is data without a structure ... And they give the most deadly example of such data - text. I wonder what my teacher of Russian language and literature would say if she found out that the Russian language / text does not have a clear structure and, as a result, years of studying it are meaningless, since we are learning rules that, according to some, do not exist.
To understand my point of view, I will give an example - the text field in Postgres. Let's say I put JSON there. For version 8, it will be just text (unstructured data), for version 9, it will already be JSON (structured data). That is, the same data is both structured and unstructured at the same time? Again unimaginable dualism from lectures on physics? The answer is simple - unstructured data does not exist, it's just that for some types of data there are no generally accepted and at the same time widespread methods for working with this data.
A literate reader, of course, will exclaim - but what about the video data? Any video is a set of frames. Every frame is an image. Images are of two types:

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  vector;
  
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  raster.
  

Calling vector images unstructured is extremely difficult. Here you can read at least about the SVG format, which is essentially XML. Raster images are in fact an array of points, each of which is described by a very clear data structure.
Huge size. I don't disagree with public opinion here. As soon as problems start with the volume of data (difficult to receive, difficult to store, difficult to process, etc.) - you have a huge size (of data). The concept is rather subjective, for me the huge size is measured in pieces. For me, the lower bound of Big Data is a million records. Rationale - queries to the DBMS with complexity of the type Θ( n2 ) on such a volume are executed for several minutes, which is a long time for me.
For other people, the justification / criterion may be different, therefore, the lower limit of the huge size will be different.