DataDoctor

Runs entirely in your browser — no uploads, no saved snippets, no public links.

Paste a file, inspect the issues, export reviewed output.

CSV structure preflight

CSV Validator

Check CSV text for malformed rows, uneven columns, broken quotes and delimiter problems before import.

Paste CSV, load the customer sample, or upload a local file.

Next workflow

Continue the preflight

CSV Data Quality Checker

Profile missing values, duplicates, invalid values and type drift.

CSV Duplicate Finder

Find duplicate rows by one or more key columns.

CSV Cleaner

Remove duplicate or incomplete rows after reviewing issues.

After the tool runs

CSV Validator review guide

Use the tool above first. The supporting notes below help you interpret the result, fix the right issues in the right order, and choose the next DataDoctor tool without pushing SEO content above the actual task.

Best input

checking CSV structure before you debug platform-specific import rules.

Output to keep

Save the original file, the issue report and the reviewed export as separate files.

Next check

After structural and quality issues are visible, run a platform checker or schema validator before upload.

What it checks

CSV Validator for real data work

CSV Validator should sit before the import screen, not after a failed upload. It turns hidden spreadsheet problems into a checklist you can review row by row.

  • Rows and columns
  • Malformed quotes
  • Uneven row lengths
  • Parser errors

Fix these first

Common errors to review before downstream work

Most failures come from small file issues that become expensive only after an API call, import job or spreadsheet cleanup. Fix blocking errors first, then re-run the same tool before moving forward.

  • Unclosed quoted fields
  • Extra delimiters
  • Rows with too few columns
  • Header/data mismatch

Recommended workflow

Run the check in this order

Treat any downloaded output as a reviewed candidate. Keep the source CSV unchanged so you can reconcile removed rows, duplicate groups or missing values later.

  1. Step 1

    Paste or upload the CSV

  2. Step 2

    Validate structure

  3. Step 3

    Fix parser errors first

  4. Step 4

    Continue to data quality or import-specific checks

How to interpret a passing result

A pass means this specific preflight did not find the issues listed above. It is not a guarantee that the target system will accept every row, field, custom mapping or account-specific rule.

Do not clean, deduplicate or drop rows before parser errors, required columns and duplicate-key logic are clear.