By Team Hypomnema · Data
Validate JSON against a supplied schema with precise error paths.
Use the guide alongside the working tool.
Open JSON Schema Validator →Explicit working sample
{"name":"Asha","age":-2}Validate JSON against a supplied schema with precise error paths. JSON Schema draft-07; supplied schema stays local. Local # references only, no remote references. No coercion, defaults or removal of fields. Format checks use Ajv draft-07 defaults. Schemas and data are bounded and processing can be cancelled. Unrecognized schema keywords are rejected.
Prepare the input
Validate JSON against a supplied schema with precise error paths.
Paste the supported structured data or log text. The sample loads only when explicitly selected and can be undone.
Review the method
JSON Schema draft-07; supplied schema stays local. Local # references only, no remote references. No coercion, defaults or removal of fields. Format checks use Ajv draft-07 defaults. Schemas and data are bounded and processing can be cancelled. Unrecognized schema keywords are rejected.
Keep the useful output
Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.
CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.
Ready to apply this workflow?
Try JSON Schema Validator →Explore a related workflow
- Dataset Drift Detector: methods and data limits
Compare numeric and categorical distributions across two datasets.
- Dataset Schema Change Checker: methods and data limits
Review observed field and type changes between JSON datasets.
- Data Dictionary Builder: methods and data limits
Build an editable field dictionary with definitions, units and ownership.
- Data Quality Profiler: methods and data limits
Inspect missingness, uniqueness, inferred types and numeric summaries.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.