Data Converter & Generator
This tool moves data between the formats developers use most — CSV, JSON, and SQL insert statements — in either direction, and doubles as a mock data generator: define a schema of field types and ToolKloud produces thousands of realistic rows for testing, demos, or seeding a database.
How it works
- 1
Paste or upload your CSV, JSON, or SQL data, or start a new mock schema.
- 2
Pick your source and target format for conversion, or define field types for generation.
- 3
Preview the converted or generated output instantly.
- 4
Copy or download the result as a file.
What people use it for
- Moving a dataset between CSV, JSON and SQL insert statements during a migration.
- Generating seed data for a development database from a defined field list.
- Producing SQL inserts from a spreadsheet export without writing a script.
- Creating realistic sample records for testing an import pipeline.
Supported formats & options
| Formats | CSV, JSON and SQL, convertible in any direction |
|---|---|
| Generator | Define fields and produce sample records in any of the three formats |
| Input | Data pasted as text |
Key advantages & benefits
Bidirectional conversion
Move freely between CSV, JSON, and SQL without needing separate tools for each direction.
Custom mock schemas
Define field names and types to generate mock data that actually matches your real table structure.
Generate thousands of rows instantly
Populate test databases or fixtures without hand-writing sample data.
Handles nested JSON
Nested objects and arrays are flattened sensibly when converting JSON to tabular formats like CSV.
Limits & things to know
- Flat, tabular records only. Nested JSON has no direct CSV or SQL column equivalent and must be flattened first.
- Generated SQL is a plain INSERT statement set — it does not create the table, add constraints or match your schema's types.
- Values are treated as text unless they are obviously numeric; check types before running generated SQL against a real database.
- Always review generated SQL before executing it. Values containing quotes need proper escaping for your specific database.
Troubleshooting
Generated SQL fails to run
Check that the table exists with matching column names, and that string values containing apostrophes are escaped for your database's dialect.
Nested JSON produced empty columns
Flatten nested objects into dot-notation keys before converting; tabular formats have no representation for nesting.
Column order changed
Order follows the keys of the first record. Ensure every record shares the same key set, in the same order, before converting.
Privacy & data handling
All conversion and generation happens in your browser. Real data pasted here — customer records, order exports, production dumps — is never transmitted to our server.
Data Format Converter — JSON, CSV, XML, YAML & SQL Online
Convert structured data between JSON, CSV, XML, YAML and SQL formats in your browser. A free data converter for moving a payload from whatever an API returned into whatever the next system expects, without writing a throwaway script.
- JSON, CSV, XML, YAML and SQL — pick a source and a target format and convert directly, including the awkward pairs like XML to JSON and YAML to CSV.
- Config file conversion — move a configuration between YAML and JSON when a tool changes its expected format, keeping nesting and types intact.
- SQL insert generation — turn a spreadsheet export into insert statements for seeding a development database.
- Structure preserved — nested objects, arrays and typed values survive the round trip rather than collapsing into strings.
- Runs locally with no upload — production data exports stay on your machine — no file is transmitted to be converted.
Free and unlimited. For a straight two-way spreadsheet workflow the CSV to JSON Converter is more direct, and the JSON Formatter validates the output.
Frequently asked questions
Can I convert deeply nested JSON to CSV?
Yes — nested fields are flattened into dot-notated columns so the structure is preserved in tabular form.
What SQL dialect does the SQL export use?
Output uses standard ANSI SQL INSERT syntax that works across MySQL, PostgreSQL, and SQLite with minimal adjustment.
How realistic is the generated mock data?
Generated values are randomized but shaped by field type (names, emails, dates, numbers, etc.) so they look and behave like real records.
Is there a row limit for generation?
You can generate very large datasets, though extremely large row counts will take longer and use more browser memory.
Does the converter handle large CSV files?
Yes, though very large files (tens of thousands of rows) may take a few seconds longer to parse and render.