Structured data

1. What’s structured data?

Structured data is a type of information organized and stored in a way that can be accessed and processed systematically. This data is presented in a predefined format with a clear and consistent structure, usually in tabular form. The structure of the data is defined by a schema, which specifies the way data elements are organized and how they relate to each other.

2. What’s the structured data function?

A common example of structured data is relational databases, where information is organized into tables with rows and columns, and relationships are established between the tables. Each field in the table has a specific type of data (numbers, text, dates, etc.), which provides order and coherence to the information. The key advantage of structured data is its ease of storage, query and analysis using structured query languages, such as SQL (Structured Query Language). This data is ideal for situations where the relationship between data elements is clear and can be modeled in table format.

Structured data is useful for maintaining an order in data, which is more useful than in the case of unstructured data, since this same order allows them to be easier to maintain, much more optimal for training Machine Learning models and They are supported by a greater number of companies and technologies due to their structure. For example, if you have a database in MongoDB, this data will be nested, which must be extracted from that database using the MongoQL query language, organized in a tabular format as SQL structured data, and later clean the data, scale it and finally split the training and test data for the Machine Learning model.

3. Example of structured data.

• Excel archives.
• Web form results
• CSV archives.
• SQL tables archives.
• Geolocalization data.
• XML archives.