Synthetic Data Without Python: Online Alternative to SDV

Most guides on generating synthetic data start with pip install. If you only have a CSV and a deadline, there is a faster route, with limits worth knowing.

Open the Synthetic Data Generator

What Python offers. Faker creates random fake values such as names and emails, but each column is independent and ignores your real data. SDV (Synthetic Data Vault) learns from a real table and can model several linked tables with advanced models. Both need Python, installation and code, and they suit pipelines and larger projects.

What an online generator offers. You load a file, check how each column will be made, and download new rows. There is no install and no code. In our tool the data stays in your browser, so the file is not uploaded. It learns the range, spread and shares of each column and how number columns move together, then compares real and synthetic numbers in a table.

Where it is enough. A realistic CSV for a demo, a dashboard test, a staging database, a bug report you cannot share real data for, or a quick look at how a model behaves on more rows. Up to 500,000 rows in one run.

Where to use Python instead. Several linked tables with foreign keys, strict business rules, repeatable scripted runs in a pipeline, or formal privacy guarantees. This tool works on one file at a time and does not offer a formal privacy guarantee, so review the output before sharing it outside your team.

Open the Synthetic Data Generator

Related tool pages

  • Generate Test Data from Production Data: Create realistic test data from a production export without copying real rows. Names and emails are replaced, patterns are kept. Runs in your browser.
  • Anonymize CSV Data for Testing: Make an anonymous version of a CSV for testing: fake names and emails, new ids, same distributions. Free and in your browser.
  • GDPR-Safe Test Data Generator: Generate test data with no real people, based on the patterns of your own file. Everything runs on your device. Not legal advice; review before sharing.
  • 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. Open the Synthetic Data Generator.
  2. Load your CSV, Excel, JSON or Parquet file.
  3. Check how each column will be made and choose the row count.
  4. Generate, compare with the real data and download.

When to use the Synthetic Data Generator

  • Get realistic test data without setting up Python
  • Make a safe sample for a bug report
  • Fill a demo or staging environment
  • Try a model on more rows quickly

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.

Is the synthetic data generator free?

Yes. There is no signup and no limit on how often you use it. It runs in your browser, so your file is never uploaded.

Which files can I use?

Load a CSV, TSV, Excel (.xlsx), JSON or Parquet file, then download the synthetic result in the format you choose.

How many rows can I generate?

Up to 500,000 new rows in one run. The default is the size of your file, between 100 and 50,000 rows.

Tip: Start with the online tool to see if the result is good enough. Move to code only when you need linked tables or scripted runs.