transform

To convert JSON to CSV while tracking lost data, provide a source payload, target schema, and optional mappings to generate deterministic output that documents any information lost during the transformation.

Data provenance in schema conversion is the explicit trace of what information is preserved, modified, or dropped when transforming structured data between formats like JSON and YAML, enabling auditable workflows.

You map nested JSON fields to a flat schema by providing explicit field mappings alongside the target schema, ensuring the transformation returns deterministic output with validation results and evidence anchors.

Schema validation works with YAML and CSV conversions by applying schema conformance checks and loss tracking across structured data, returning deterministic output that conforms to the target schema.

Dropped fields during a data format conversion are explicitly documented in the provenance trace, allowing you to audit what information was modified or lost when applying explicit field mappings to the target schema.

You can transform nested objects between YAML and JSON deterministically by applying explicit field mappings to produce schema-compliant output with attached validation results and full provenance tracking.