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Glossary
Data Infrastructure

Semi-structured data

Semi-structured data is data that has a certain order and organization but does not follow strict rules.

Meaning in detailContact us now

In contrast to unstructured data, such as text documents or images, semi-structured data can be processed by machine.

Characteristics of semi-structured data

  • No rigid requirement: Semi-structured data does not follow a rigid schema or data model.
  • markings: Tags such as XML tags or HTML elements are often used to structure the data.
  • Flexibility: The structure of data can be flexible and change over time.
  • Machine processing: Semi-structured data can be processed automatically using suitable methods.

Examples of semi-structured data

  • XML files: XML is a common language for storing and transmitting semi-structured data.
  • JSON files: JSON is another popular language for semi-structured data.
  • Emails: Emails often contain text, HTML elements, and attachments, making them semi-structured data.
  • Log files: Log files contain records with various information, such as time stamps, events, and error messages.

Benefits of semi-structured data

  • Flexibility: Semi-structured data is more flexible than structured data and can adapt to new requirements.
  • Processability: Semi-structured data can be processed automatically using suitable methods.
  • Storage space: Semi-structured data can be stored in a more space-saving way than structured data.

Disadvantages of semi-structured data

  • Complexity: Processing semi-structured data can be more complex than processing structured data.
  • Susceptibility to errors: Semi-structured data is more susceptible to errors than structured data.
  • Interoperability: The interoperability of semi-structured data may be limited.

More variants

Data-related services can be found, for example, in Data Strategy, data management Or even Data Governance.

Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.

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Data Architecture

The right tools in the right place. Update the existing infrastructure or renew most of it.

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Data Organization

So that the handling and responsibility of data is structured and everyone knows its responsibilities.

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