How is semi-structured data different from structured data?

Semi-structured data is characterized by its ability to have some organizational properties while not adhering to a fixed schema. This means it is not as rigidly organized as structured data, which fits neatly into tables with defined rows and columns, but it still contains tags or markers that help to separate different elements. Examples of semi-structured data include JSON documents, XML files, and email messages, which have a consistent structure but can vary in format and content.

The key difference lies in that semi-structured data allows for more flexibility in terms of its structure, accommodating varying data types and formats without needing a predefined model. This flexibility enables semi-structured data to be more adaptable, accommodating diverse and complex data without requiring every record to fit a specific template.

In contrast, structured data is highly organized and easily searchable in relational databases, relying on a strict schema. Other options do not accurately describe semi-structured data; it is indeed analyzable and often requires processing before storage to ensure that the data is usable.