CSV Validator

Paste CSV and get structural findings: duplicate headers, inconsistent column counts, invalid dates/numbers, duplicate rows, plus a preview table and JSON export.

Runs 100% client-side
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On this page5 sections

HOW TO USE

  1. 01Paste your CSV into the input area.
  2. 02Review the structural findings — duplicate/missing headers, inconsistent column counts, invalid typed values, duplicate rows.
  3. 03Switch to the JSON preview to confirm how the data will parse downstream.
  4. 04Copy the findings or the JSON before sending the file to the system under test.

Try it

Paste a row with a quoted value like "1,234.56" to confirm commas inside quotes parse

WHEN TO USE

Use when testing file-upload flows, data-import pipelines, or ETL processes that accept CSV input. Paste the CSV to get structural findings (duplicate/missing headers, row-length inconsistencies, invalid typed values, duplicate rows) before sending it to the system under test — or to diagnose why an upload was rejected.

WHAT BUGS THIS FINDS

  • Header mismatch

    Column names differ between the test file and the schema the system expects — import silently maps to the wrong columns.

  • Inconsistent column count

    A row with an unescaped comma inside an unquoted field adds a phantom column, breaking all subsequent field mappings.

  • Invalid typed values

    A date field contains a string ('N/A', 'TBD') that passes visual inspection but causes a parse error in the import pipeline.

  • Duplicate rows

    Test data files with duplicate rows cause idempotency checks to fail or seed duplicate records in the test database.

QA USE CASES

01

Validate a test data file before upload

Catch duplicate headers, inconsistent column counts, and blank required fields before the CSV reaches the upload endpoint.

02

Diagnose a rejected import

Paste the file that the system rejected — findings pinpoint the exact row and column that triggered the error.

03

Generate a JSON preview

Convert the CSV to JSON to verify the parsed structure matches what the API or downstream system will see.

04

Check data quality for test data sets

Validate that a generated test data CSV has no duplicate rows, valid date formats, and no empty required fields.

Related

Guides

  • CSVcoming soon