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Semi-structured data is a type of data that doesn't fit neatly into traditional relational databases but has some organizational properties that make it easier to analyze compared to unstructured data. A few characteristics of semi-structured data includes some metadata that identifies certain characteristics:

• Presence of Metadata: Semi-structured data often includes metadata that describes the data in some way. This metadata can be used to identify and classify parts of the data. For instance, in an XML or JSON document, the tags and keys provide information about the data fields they contain.
• Hierarchical or Tag-based Structure: Semi-structured data is usually organized in a way that reflects a hierarchy or uses tags to separate data elements. For example, JSON uses keys to associate values in a nested structure, which helps in identifying and accessing data components.
• Lack of Rigid Schema: Unlike structured data, semi-structured data does not conform to a rigid, predefined schema. This allows for more flexibility in the data, but it can also introduce variability that might complicate data management and analysis.
• Ease of Data Manipulation: The inherent structure in semi-structured data (like XML and JSON) often makes it easier to manipulate programmatically, especially compared to unstructured data like plain text or images.

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