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Semi-structured data describes a model between strictly structured tables and unstructured text, allowing flexible yet partially organized information representation. Common formats include JSON, XML and YAML; hybrid models like JSON-LD or RDF are also used. The concept enables agile integration and adaptability but requires validation, indexing and transfor…
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Semi-structured data has recognizable structure through tags, keys, or nested relationships without following a completely rigid table or schema format.
The term became useful for document and interchange formats between free text and strictly relational tables. XML was developed as standardized, extensible markup for structured documents and data exchange.
A document carries data together with names and hierarchy: elements can be nested and repeated, while attributes add detail. A parser reads the structure; schema or validation rules can define which variants are allowed.
Names, keys, and nesting carry structure even when every shape is not fixed in advance.
Documents can contain varying fields and depths, easing evolution and integration.
Schemas and rules check which concrete form of a flexible document is accepted.
Semi-structured data suits changing domains and document-oriented interfaces. Agreements on versioning, required fields, data types, and validation are vital for reliable integration and analysis.
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