By Team Hypomnema · Data
Check records against clear rules and separate passing and failing rows.
Use the guide alongside the working tool.
Open Data Validation Rules →Explicit working sample
id,region,product,amount,units,date
001,North,Notebook,24.5,2,2026-10-01
002,South,Pen,9,3,2026-10-02
003,North,Pen,15,5,2026-10-03
004,West,Notebook,,1,2026-10-04
005,South,Notebook,49,4,2026-10-05
006,North,Pen,1200,2,2026-10-06Check records against clear rules and separate passing and failing rows. Rules are applied to supplied data only. Supported checks: required, unique, number, integer, range, allowed, email, http URL and ISO date. Unique checks flag every duplicated nonempty value. Rules are not JSON Schema validation; email checks validate basic structure, not deliverability.
Prepare the input
Check records against clear rules and separate passing and failing rows.
CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.
Review the method
Rules are applied to supplied data only. Supported checks: required, unique, number, integer, range, allowed, email, http URL and ISO date. Unique checks flag every duplicated nonempty value. Rules are not JSON Schema validation; email checks validate basic structure, not deliverability.
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 Data Validation Rules →Explore a related workflow
- Data Quality Profiler: methods and data limits
Inspect missingness, uniqueness, inferred types and numeric summaries.
- Missing Value Cleaner: methods and data limits
Fill or remove missing values with a complete, reviewable change log.
- Outlier Detector: methods and data limits
Flag unusual numeric values using IQR fences, z-scores or median deviation.
- Fuzzy Record Matcher: methods and data limits
Compare approximate record names and review ranked match candidates.
Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.