simonaubertbd opened a new issue, #48959: URL: https://github.com/apache/arrow/issues/48959
### Describe the enhancement requested Hello, As data increasingly moves across organizational and regulatory boundaries, data sensitivity is becoming just as important as data type. Today, teams often need to answer questions like: -Does this dataset contain personal data? -Which specific fields are subject to GDPR/CCPA or internal governance rules? -Can this column be safely logged, cached, or shared downstream? In practice, this information is either: Stored out-of-band (data catalogs, documentation), or Embedded in ad-hoc metadata conventions that vary by organization and tool. A simple, standardized personal_data boolean at the field level would provide a lightweight, interoperable signal that many tools could immediately benefit from. Field-level granularity is essential: most real datasets mix personal and non-personal columns. A boolean keeps the signal intentionally minimal. This would enable: Automatic detection and propagation of personal data flags across Arrow-compatible systems Safer defaults in query engines, serializers, and exporters Easier integration with data catalogs, lineage tools, and privacy audits Consistent behavior across Arrow consumers Crucially, this does not enforce semantics or compliance — it simply provides a common language. This would be entirely optional and backward-compatible. It does not preclude richer classifications in external systems. It aligns with Arrow’s existing use of key/value metadata without introducing new core types. Best regards, Simon ### Component(s) Python -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
