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Mock CSV Data Generator

Used by developers, writers, and creators worldwide.

A mock CSV data generator gives developers structured, realistic comma-separated test data without touching a live database or hand-crafting rows. Pick a dataset type — user profiles, products, or transactions — set the row count, and get clean, header-correct CSV output in seconds. Generated values follow plausible real-world patterns: names, emails, prices, and timestamps that resemble production data, not strings like "user1" or "test@test.com". That matters because realistic data surfaces edge cases your validation logic, UI components, and import pipelines will eventually hit. Spreadsheet tools, ORM seeders, ETL pipelines, and Postman data-driven tests all accept CSV natively, so the output plugs straight into your existing workflow.

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How to use

  1. Choose your options above
  2. Click Generate
  3. Copy your result

Detailed instructions

  1. Select a dataset type from the Fields dropdown — choose 'user', 'product', or 'transaction' based on what your feature needs.
  2. Set the number of rows using the Rows input; start with 10-20 rows for quick checks or go up to 100 for more thorough testing.
  3. Click Generate to produce the CSV output with a properly formatted header row and realistic data in every column.
  4. Copy the output and paste it into a .csv file, a spreadsheet import dialog, or directly into your database seed script.

Use Cases

  • •Seeding a PostgreSQL dev database with 100 user rows using psql \copy and auto-mapped headers
  • •Testing a CSV file import wizard with correctly formatted headers and varied field values
  • •Populating an AG Grid or TanStack Table component with realistic rows during UI prototyping in Storybook
  • •Generating a Postman data-driven test file to hit a REST endpoint with dozens of unique transaction payloads
  • •Committing fixture files to a repo so every developer runs Jest or Vitest tests against identical, stable data

Tips

  • →Run the generator twice with different row counts and merge the outputs to build a larger dataset without duplicating headers.
  • →Paste the output directly into Google Sheets via File > Import > Paste text to instantly visualize the data structure before using it in code.
  • →For database seeding, match the CSV column names to your table's column names first — renaming headers now is faster than fixing an import error later.
  • →Use transaction data to test sorting and filtering logic, since it includes numeric and date fields that expose bugs that string-only data won't.
  • →When testing CSV import UI, try uploading the raw generated output without editing it — if your parser fails on clean RFC-standard CSV, the bug is in your parser.
  • →For frontend table components, generate 50+ rows to test pagination, scroll performance, and column overflow rendering under realistic data volume.

FAQ

how do I import mock CSV data into PostgreSQL or MySQL quickly

Save the output as a .csv file. In PostgreSQL, use the \copy command in psql or the import wizard in pgAdmin or TablePlus — both auto-map header names to table columns. In MySQL, use LOAD DATA INFILE or mysqlimport. If you're on an ORM like Prisma or Sequelize, a short seed script that reads the file with Node's fs module and bulk-inserts rows is usually the fastest path.

is synthetically generated CSV data safe to commit to a public repo

Yes. Every value is entirely fictional — names, emails, IDs, and prices are generated algorithmically and contain no real personal information, so there's no GDPR or privacy risk. That's a key advantage over anonymizing a production dump, which can still leak structural patterns or outlier values if done carelessly.

what's the difference between the user, product, and transaction field presets

Each preset generates a different column mix tuned for that domain. User outputs name, email, and ID fields for testing auth flows and profile pages. Product includes SKU, price, and category columns for e-commerce or inventory UIs. Transaction outputs date, amount, and status fields, ideal for testing dashboards, filters, and sorting logic.