Generate Test Data as Parquet Online

Test data should arrive in the format your system reads. The generator can download Parquet directly.

Open the Mock Data Generator

Open the Mock Data Generator, add the columns you need with their data types, set the number of rows (up to 500,000) and choose the format when you download.

Parquet keeps a type for every column and is much smaller than CSV, so it suits pipeline and warehouse tests with many rows.

Names, emails, dates, numbers and ids look realistic, but they are made up, so the file is safe to share. Nothing is uploaded.

Everything runs in your browser and nothing is uploaded.

Open the Mock Data Generator

Related tool pages

  • Fake Data Generator: Generate realistic fake data online: names, emails, addresses, dates and more, up to 500,000 rows, as CSV, JSON, Parquet or Excel. Free.
  • Test Data Generator: Generate test data for databases and apps: names, emails, addresses, dates and numbers, up to 500,000 rows, as CSV, JSON, Parquet or Excel. Free.
  • Fake Database for Testing: Create a fake database for testing: paste a CREATE TABLE script and get realistic rows as INSERT statements, CSV or JSON. Free, in your browser.

How to use the Mock Data Generator: step by step

  1. Open the Mock Data Generator.
  2. Add columns and types.
  3. Set the number of rows.
  4. Download Parquet.

When to use the Mock Data Generator

  • Fill a test database
  • Demo a dashboard
  • Load a pipeline test
  • Share a sample without real data

Example

Input: Dataset: Customers, 3 rows

Output: id,name,email 1,Maya Patel,maya.patel@example.com 2,Leo Fischer,leo.fischer@example.com

Frequently asked questions

Is the data real?

No. It is random and fictional, so it is safe to share.

Can I get the same data again?

No – each click makes new data. Download to keep a set.

How many rows can the Data Generator create?

Up to 500,000 rows at a time. Choose column types such as names, emails, addresses and dates, then download as CSV, JSON, Parquet or Excel.

Tip: Use Parquet to test pipelines and Spark or DuckDB jobs with large volumes.