DataDoctor

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

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

CSV schema generation

CSV Schema Generator

Infer a starter JSON Schema from CSV headers and sample values before validating future files.

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

Next workflow

Continue the preflight

CSV Schema Validator

Validate CSV rows against a schema.

Schema Checker

Validate JSON or CSV data against JSON Schema.

CSV Data Quality Checker

Profile column quality before locking a schema.

After the tool runs

CSV Schema Generator 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

inferring a starter schema from representative CSV rows before creating a stricter import contract.

Output to keep

Keep the schema, sample data and validation report together so contract changes are traceable.

Next check

Refine required fields, enums and type rules, then validate a second sample before relying on the contract.

What it checks

CSV Schema Generator for real data work

CSV Schema Generator is for contract work: it checks whether the data shape matches the rules another system expects. Use it after the raw JSON or CSV parses cleanly.

  • Column names
  • Inferred field types
  • Required columns
  • Array-of-objects schema output

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.

  • Sparse samples that hide optional fields
  • Mixed data types in one column
  • Blank values changing required status
  • Using generated schemas without review

Recommended workflow

Run the check in this order

When the report points to a path or row, fix the data when the source is wrong and fix the schema only when the contract was written too narrowly.

  1. Step 1

    Paste representative CSV rows

  2. Step 2

    Generate the schema

  3. Step 3

    Review types and required fields

  4. Step 4

    Use the schema validator on future files

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.

A generated or passing schema is not a final data contract until optional fields, nullable values and edge cases are reviewed.