Explain the difference between structured, semi-structured, and unstructured data and give an example for each in CDX.

Explain the difference between structured, semi-structured, and unstructured data and give an example for each in CDX.

• A. Structured data is untagged in flat files; semi-structured data is fully schema-driven; unstructured data is always binary.
• B. Structured data is schema-based in tables; semi-structured data has tags (JSON/XML); unstructured data has no inherent schema (free text, images).
• C. Structured includes images; semi-structured includes binary; unstructured is CSV.
• D. Structured and semi-structured are the same; unstructured uses XML.

Answer: (B)

Structured data is organized around a fixed schema, typically stored in tables where every record follows the same set of fields. In CDX, that looks like a relational table of catalog records with columns such as item_id, title, artist, release_year, and genre, so queries can rely on the consistent column structure. Semi-structured data has some organizational cues, like tags or markers, but it isn’t confined to a single rigid schema. In CDX this is common with JSON documents or XML manifests that describe items with labeled fields and potentially nested sections; you can read the data meaningfully even if different items include different optional fields. Unstructured data has no predefined schema and is usually free-form content or binary data. In CDX this includes things like high-resolution artwork files (images), audio or video tracks, scanned pages, or plain text notes about assets, where there isn’t a consistent table-like structure to rely on for automatic parsing. So, the best description aligns with a schema-based, table-wide organization for structured data; self-describing tags or markers for semi-structured data; and content without a fixed schema for unstructured data. The examples reflect typical CDX usage: a fixed-column catalog table for structured data, JSON/XML descriptors for semi-structured data, and media files or free-form notes for unstructured data.

Structured data is organized around a fixed schema, typically stored in tables where every record follows the same set of fields. In CDX, that looks like a relational table of catalog records with columns such as item_id, title, artist, release_year, and genre, so queries can rely on the consistent column structure.

Semi-structured data has some organizational cues, like tags or markers, but it isn’t confined to a single rigid schema. In CDX this is common with JSON documents or XML manifests that describe items with labeled fields and potentially nested sections; you can read the data meaningfully even if different items include different optional fields.

Unstructured data has no predefined schema and is usually free-form content or binary data. In CDX this includes things like high-resolution artwork files (images), audio or video tracks, scanned pages, or plain text notes about assets, where there isn’t a consistent table-like structure to rely on for automatic parsing.

So, the best description aligns with a schema-based, table-wide organization for structured data; self-describing tags or markers for semi-structured data; and content without a fixed schema for unstructured data. The examples reflect typical CDX usage: a fixed-column catalog table for structured data, JSON/XML descriptors for semi-structured data, and media files or free-form notes for unstructured data.