Dev
Mock CSV Data Generator
Hand-crafted CSV test data is tedious to write and often too uniform to catch real bugs. Values like 'test1' or 'test2' miss the variance production data always has — variable name lengths, decimal prices, and varied statuses all affect validation logic and import pipelines differently. Two inputs control the output. Fields preset selects the schema: user outputs id, first_name, last_name, email, age, and city; product outputs id, product_name, category, price (decimal), stock, and SKU; transaction outputs id, date (ISO format), amount (decimal), currency, status, and reference (TXN-prefixed). Rows sets the count from 1 to 100. Output always includes a header row, so it imports directly into PostgreSQL via \copy, MySQL via LOAD DATA INFILE, or any spreadsheet tool without a manual header step. For larger seed datasets, run multiple batches and concatenate outputs.
How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Select a dataset type from the Fields dropdown — choose 'user', 'product', or 'transaction' based on what your feature needs.
- 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.
- Click Generate to produce the CSV output with a properly formatted header row and realistic data in every column.
- 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. For Prisma or Sequelize, a short seed script that reads the file with Node's fs module and bulk-inserts rows is 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. This is a key advantage over anonymizing a production dump, which can still leak structural patterns or outlier values.
what is the difference between the user, product, and transaction field presets
User outputs id, first_name, last_name, email, age, and city — for testing auth flows and profile pages. Product outputs id, product_name, category, price, stock, and sku — for e-commerce or inventory UI testing. Transaction outputs id, date, amount, currency, status, and reference — for dashboards, filters, and sorting logic. Presets are fixed; you cannot add or remove columns within a preset.
can I generate more than 100 rows at once
The generator caps at 100 rows per run. For larger datasets, run the generator multiple times and concatenate the output. Since each run produces a fresh header row, paste only the first run's header and strip headers from subsequent runs before merging.
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