Semi-structured data refers to formats that do not follow a rigid relational schema but still contain structural tags or hierarchies, allowing flexible representation of nested or irregular data. In Snowflake,JSON,Parquet, andXMLare all considered semi-structured because they incorporate metadata, hierarchical fields, or tagged data that can vary across records. JSON offers key--value pairs and nested arrays, making it ideal for logs and API responses. Parquet, being a columnar file format containing both schema definitions and metadata, is optimized for analytics while still supporting semi-structured capabilities such as variable field nesting. XML uses tags and attributes to represent hierarchical content, making it semi-structured as well. On the other hand, CSV represents strictly structured, row/column-based data without inherent metadata or hierarchy, so it is not considered semi-structured. Snowflake treats semi-structured formats by loading them into the VARIANT data type, enabling powerful SQL-based exploration using path notation.

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