The data can be classified according to its origin, rank, the type of language we want to work with, etc. The most practical and global classification that allows us to work effectively in the digital world is based on its structure. According to this, there are 3 types of data: structured, semi-structured and unstructured.
Structured data is the typical data of most relational databases (RDBMS). These databases are characterised by having a specific schema that defines what the tables in which the data is stored look like, what type of fields they have and how they relate to each other.
This data is managed using a type of structured programming language, known as SQL (Structured Query Language) designed precisely to administer and recover information from relational database management systems.
It is the structured data that is the easiest to handle, as it is all in the same format. They are usually text files that are stored in the form of tables, spreadsheets or relational databases in which each category is identified by a title.
An example of this type of data is financial data or data generated by IoT sensors.
It is very important to be aware of the type of data handled in each case, to decide which resources and tools are most appropriate for each situation. This will allow us to define the most efficient architectures that cover the needs of a company with the best cost-benefit ratio.
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