How to Generate Synthetic Data From a CSV, Step by Step

A walk through the Synthetic Data Generator, from a real file to a safe, realistic one.

Step 1: load your file. Drop a CSV, Excel, JSON or Parquet file into the Synthetic Data Generator. It is read in your browser and never uploaded. More real rows give better results; under 100 rows gives only a rough copy of the patterns.

Step 2: check each column. The tool lists every column and what it will do: number and date columns keep their range and how they move together, categories keep their shares, names and emails get new fake values, ids are renumbered, and free text gets the same shape. Tick Leave out for columns you do not need, such as notes.

Step 3: choose the size and generate. Enter how many new rows you want, up to 500,000, keep Hide rare values on so that unusual values become Other, and click Generate. A test with 3,000 rows kept the average age and salary within about one percent of the real file, and the relationship between age and salary within 0.03.

Step 4: check and download. Compare the real and synthetic numbers in the table, look at the preview and download CSV, Excel, JSON or Parquet. Review the data before sharing it publicly, because synthetic data lowers risk but is not a guarantee.

Open the Synthetic Data Generator

Related tool pages

  • Generate Synthetic Data from a CSV: Upload a CSV and generate synthetic rows that match its distributions and number relationships. Names and emails are replaced. Free, no upload.
  • Privacy-Safe Test Data from Real Data: Create test data that looks like production without copying production rows. Names and emails are replaced and rare values are hidden. Free, no upload.
  • Synthetic Dataset from an Excel File: Turn an Excel file into a synthetic dataset with the same patterns and no real records. Choose how many rows and download CSV, Excel or Parquet. Free.

How to use the Synthetic Data Generator: step by step

  1. Load your real file.
  2. Check and adjust the columns.
  3. Set the number of rows and generate.
  4. Compare, then download.

When to use the Synthetic Data Generator

  • Create test data from a production export
  • Make a shareable sample of a private dataset
  • Generate a bigger dataset than you have
  • Prepare demo data for a customer

Example

Input: real customers.csv

Output: new rows that behave like it

Frequently asked questions

What is synthetic data?

Data made by a program that follows the patterns of real data, such as ranges, shares and how columns move together, without containing the real records.

How is it different from the Data Generator?

The Data Generator makes random rows from column types you choose. The Synthetic Data Generator learns from your own file and makes rows that match its patterns, so the results look like your real data.

Is synthetic data fully anonymous?

It lowers the risk, and no real row is copied, but it is not a guarantee. Review it before you share it publicly.

How is the Synthetic Data Generator different from the Data Generator?

The Data Generator makes random rows from column types you choose, such as names, emails and dates, so the columns do not relate to each other. The Synthetic Data Generator learns from your own file and makes rows that match its ranges, shares and the way number columns move together, so the result looks like your real data. Use the Data Generator for quick fake data, and the Synthetic Data Generator when the data should behave like production.

Tip: Generate twice with different row counts. If both match the real numbers, the model of your data is stable.