What column types are supported?

You can choose first name, last name, full name, email, integer, decimal, boolean, date, city, company, and UUID. Each row gets a fresh random value of the chosen type, with email and company kept consistent within a row where relevant.

Is the CSV properly escaped?

Yes. Any value containing a comma, double quote, or newline is wrapped in double quotes and internal quotes are doubled, following RFC 4180. This keeps the file parseable by spreadsheets and CSV libraries.

How many rows can I generate?

Up to 10,000 rows in the browser. Very large counts are generated in memory and offered as a download, so they will not be pasted into the preview, which is capped to keep the page responsive.

Can I download the file directly?

Yes. The Download button creates a Blob and triggers a .csv download with your chosen filename. The data is built entirely in your browser, so nothing is uploaded.

Is the data realistic?

It draws from curated name, city, and company word lists and standard formats for dates, UUIDs, and numbers, so the output looks plausible for demos and import tests while remaining entirely fake.

What is the Mock CSV Generator?

Produces CSV files with configurable column types and row counts using realistic fake data — names, emails, numbers, dates, booleans, and UUIDs. Great for testing data-import pipelines, spreadsheets, and ETL jobs. It runs free in your browser on Gera Tools, with nothing uploaded.

Mock CSV Generator — Gera Tools

Name: Mock CSV Generator
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

A mock CSV generator builds downloadable CSV test data with the exact columns and row count you specify, drawing on realistic fake values. It is built for engineers testing import pipelines, ETL jobs, spreadsheet tooling, and database seeders who need clean, well-formed sample data fast.

How it works

You define a schema and the tool fills it:

Add one column per field and choose its type (name, email, integer, decimal, boolean, date, city, company, UUID, and more).
Set the row count.
For each row, every column produces a fresh random value of its type. Numbers respect a sensible range, dates fall within a recent window, and UUIDs follow the version-4 format.
The rows are serialized to CSV with proper RFC 4180 escaping: values containing a comma, quote, or newline are wrapped in double quotes and any internal quotes are doubled.

The result is shown as a preview and offered as a .csv download or a copy-to-clipboard block.

Tips and notes

Keep column header names simple (letters, digits, underscores) so they map cleanly to database fields.
For large datasets, prefer the download over the clipboard — the preview is capped for performance, but the file contains every requested row.
All generation happens locally; no data leaves your browser, so it is safe for sensitive testing environments.

Where mock CSVs are most useful

Testing data-import pipelines

Import APIs and ETL pipelines often have silent failure modes that only surface with specific data shapes. Generating several thousand rows of varied data — including names with apostrophes, emails with plus-addressing, and decimal numbers with trailing zeros — exposes escaping bugs, column-count mismatches, and type coercion edge cases that a handful of hand-crafted rows never would.

Populating development and staging databases

Writing seed data manually is tedious and produces unrealistically uniform data (everyone lives in “New York”, every email is “[email protected]”). Mock CSVs with genuinely varied names, cities, and companies produce a dev database that behaves more like production, which catches query bugs that uniform data hides.

Spreadsheet formula testing

When testing VLOOKUP, INDEX/MATCH, or pivot tables in Excel or Google Sheets, you need enough rows to surface edge cases: duplicates, blanks, date-range boundaries, and mixed numeric formats. Generating 500 or 1,000 rows of realistic mixed-type data in one download is faster than constructing it cell by cell.

What RFC 4180 escaping means in practice

The CSV standard (RFC 4180) requires that any field containing a comma, a double-quote, or a line break must be enclosed in double quotes, and any double-quote within that field must be represented as two consecutive double-quotes (""). This generator applies that rule automatically, which means output can be fed directly to any compliant CSV parser — including Python’s csv module, Node’s csv-parse, and standard SQL COPY commands — without pre-processing.

A practical example: a company name like O'Brien & Sons, LLC becomes "O'Brien & Sons, LLC" in the file because it contains a comma. A company named The "Real" Deal becomes "The ""Real"" Deal". If you open the CSV in Excel or Numbers and those names display correctly, the escaping worked.