Dev
Generator für Mock-CSV-Daten
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.
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. 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.
How do I import mock CSV data into PostgreSQL or MySQL?
Generate the rows, save them as a .csv file, then bulk-load: in PostgreSQL use \copy table from 'file.csv' with csv header; in MySQL use LOAD DATA INFILE ... FIELDS TERMINATED BY ',' IGNORE 1 ROWS. Create a table whose columns match the CSV headers first. The output includes a header row, so the column mapping lines up.
What is the difference between the field presets?
Each preset shapes the columns to a common entity — user (id, name, email, age, city), product (sku, name, price, stock), transaction (id, amount, date, status), and so on — so the CSV matches the kind of data you are testing. Pick the preset closest to your schema, then rename or trim columns to fit exactly.
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