Semistructured

Semistructured refers to data that do not conform to a rigid, fixed schema such as a traditional relational database, yet contain tags, keys, or markers that provide context and enable interpretation of the data. This places semistructured data between structured data and unstructured data, offering more flexibility than fixed schemas while retaining a degree of organization.

Characteristics of semistructured data include a flexible or evolving schema, self-describing content, and often a hierarchical or graph-like structure. Elements are typically grouped by labels, keys, or tags, and the same dataset may contain records with varying sets of fields. This allows for easy addition of new data types without altering an overarching schema, at the cost of more complex validation and querying compared to strictly structured data.

Common formats that embody semistructured data are XML, JSON, and YAML, which encode data using tags or key-value pairs. HTML also contains semistructured information. Semistructured data is widely used for data interchange, web data extraction, logs, metadata, and various NoSQL data stores, where the emphasis is on flexible representation rather than a fixed table schema.

Advantages of semistructured data include adaptability to evolving requirements, ease of ingestion from diverse sources, and suitability for big data environments. Limitations include challenges in enforcing data quality, more complex querying and indexing, and potential performance overhead compared with fully structured data. Access typically relies on schema-on-read approaches and specialized query tools that navigate hierarchical or tag-based structures.