Jaconir

How to Generate Synthetic Data Online Free (No Code, No Setup)

March 23, 2026
9 min read

Synthetic data is artificially generated data that mimics the statistical properties and structure of real data — without containing any actual user information. It's used by developers to test applications, by data scientists to train and validate ML models, and by teams that need realistic datasets before real data is available. This guide covers when to use synthetic data, what types exist, and how to generate a complete dataset in under a minute with no code and no setup.

Generate a complete synthetic dataset instantly: Jaconir Synthetic Data Factory — configure your schema, choose data types, set row count, and export as JSON or CSV. Free, browser-only, no account needed.

What Is Synthetic Data and When Do You Need It?

Synthetic data solves a specific problem: you need data that looks and behaves like real data, but you can't or don't want to use actual user data. The most common use cases:

  • Development and testing: Your app needs a database full of users, orders, or products to test against. You don't have real users yet, or you don't want to use production data in your dev environment.
    • UI prototyping: Designing a dashboard or data table looks completely different with realistic data vs placeholder "John Doe" entries. Synthetic data makes prototypes feel real.
    • AI and ML model training: Training a model requires large amounts of labelled data. Synthetic data supplements scarce real-world datasets.
    • Performance testing: Load testing an API or database requires generating thousands or millions of realistic records to simulate production traffic.
    • Privacy compliance: Sharing real user data with third-party vendors, contractors, or external developers violates GDPR and similar regulations. Synthetic data has no privacy implications.
    • Edge case testing: Real data rarely contains the edge cases you need to test. Synthetic data lets you deliberately generate records with null values, extreme numbers, long strings, or unusual combinations.

How to Generate Synthetic Data Free (Step by Step)

Step 1: Define Your Schema

Before generating data, decide what fields your dataset needs. Think about the real data it's replacing:

  • A user table: id, name, email, phone, country, created_at, role
    • An orders table: order_id, user_id, product_name, quantity, price, status, order_date
    • A products table: id, name, category, price, sku, in_stock, description

Step 2: Open the Generator and Configure Fields

  • Open Jaconir Synthetic Data Factory
    • Add fields for each column in your schema
    • Set the data type for each field (name, email, number, date, boolean, UUID, enum, etc.)
    • Configure field-specific settings — e.g. for numbers: min/max range; for dates: start/end date; for enums: the list of possible values
    • Set the number of rows (10 for quick prototypes, 1000+ for load testing)

Step 3: Generate and Export

  • Click Generate — the tool produces your full dataset in the browser
    • Preview the first few rows to confirm the data looks correct
    • Export as JSON (for API testing and JavaScript apps) or CSV (for spreadsheets, databases, and Python)
    • Use immediately — paste into your app, import to a database, or feed to your model

Supported Data Types and What They Generate

The Synthetic Data Factory supports these field types:

  • Full Name — Realistic first + last name combinations (e.g. "Maya Patel", "James O'Brien")
    • First Name / Last Name — Separate first and last name fields
    • Email — Formatted email addresses matching the name field
    • Phone — Formatted phone numbers in international format
    • UUID / ID — Unique identifiers (UUID v4 format or sequential integers)
    • Number — Integer or float within a configurable min/max range
    • Boolean — true/false with configurable probability weighting
    • Date — ISO format dates within a configurable date range
    • DateTime — Full timestamp with time component
    • Enum — Random selection from a list you define (e.g. ["active", "inactive", "pending"])
    • Country — Country names or ISO codes
    • City — City names from a global dataset
    • URL — Formatted web URLs
    • Lorem Text — Placeholder paragraph text
    • Hex Color — CSS hex color values

Practical Examples

Generating Test Users for a Web App

Schema configuration:

  • id — UUID
    • name — Full Name
    • email — Email
    • country — Country
    • role — Enum: ["admin", "user", "moderator"] (weight user at 80%)
    • is_active — Boolean (true at 85% probability)
    • created_at — DateTime (range: 2023-01-01 to 2026-01-01) Generate 500 rows, export JSON. Seed directly into your dev database or use as mock API response data.

Generating E-commerce Orders for Load Testing

  • order_id — UUID
    • customer_name — Full Name
    • product — Enum: ["T-Shirt", "Jeans", "Jacket", "Shoes", "Hat"]
    • quantity — Number (min: 1, max: 10)
    • price — Number (min: 9.99, max: 299.99, float)
    • status — Enum: ["pending", "shipped", "delivered", "cancelled"]
    • order_date — Date (range: last 12 months) Generate 10,000 rows, export CSV. Import to your database for load testing your order processing pipeline.

Generating Labelled Data for ML Classification

  • id — UUID
    • text — Lorem Text
    • label — Enum: ["positive", "negative", "neutral"] (equal weighting)
    • confidence — Number (min: 0.5, max: 1.0, float)
    • source — Enum: ["twitter", "reddit", "news", "review"] Generate 2,000 rows as a synthetic training dataset for a text classification model.

Importing Synthetic Data Into Your Tools

JavaScript / Node.js

// Import exported JSON directly
import users from './synthetic-users.json';

// Or fetch from a local server
const users = await fetch('/data/synthetic-users.json').then(r => r.json());

// Use as mock API response
app.get('/api/users', (req, res) => {
  res.json(users.slice(0, 20)); // Paginate
});

Python / Pandas

import pandas as pd

# Load CSV export
df = pd.read_csv('synthetic-orders.csv')

print(df.head())
print(df.describe())  # Check statistical distribution
print(df['status'].value_counts())  # Check enum distribution

SQL Database (PostgreSQL / MySQL)

-- Import CSV directly
COPY users(id, name, email, country, role, is_active, created_at)
FROM '/path/to/synthetic-users.csv'
DELIMITER ','
CSV HEADER;

-- Or use the JSON export with a script to bulk insert

Postman / API Testing

Export JSON → In Postman, go to the request body → raw → JSON → paste the synthetic data directly as the request payload for POST endpoint testing.

Synthetic Data vs Faker Libraries

If you're a developer comfortable with code, faker libraries like Faker.js (JavaScript) or Faker (Python) produce similar results programmatically. The browser-based generator is faster for:

  • Non-developers (designers, PMs, data analysts) who need test data without writing code
    • Quick one-off datasets without setting up a script
    • Generating data in a browser during a meeting or prototyping session
    • Teams without a local development environment set up For large-scale, repeatable, or customised data generation integrated into a CI pipeline, a faker library is more appropriate.

FAQ

Is synthetic data the same as fake data?

Functionally yes for most development use cases. "Synthetic data" in the ML/AI context implies the data was generated to match statistical properties of a real dataset. "Fake data" is a looser term for any generated test data. For development and testing purposes the distinction doesn't matter — both serve the same purpose.

Can I use synthetic data to train AI models?

Yes, with caveats. Synthetic data works well for training classification models on structured data, augmenting scarce real datasets, and testing model pipelines. It works poorly when the model needs to learn from real-world distributions that are difficult to replicate synthetically — such as image recognition or natural language nuance.

Is synthetic data GDPR compliant?

Yes — synthetic data contains no real personal information and is not subject to GDPR, CCPA, or similar privacy regulations. This is one of the primary reasons organisations use it: share realistic data with contractors or vendors without any privacy risk.

How many rows can I generate at once?

The browser-based tool handles thousands of rows comfortably. For very large datasets (100k+ rows), consider using a faker library script which can generate and write to file without browser memory constraints.

Conclusion

Synthetic data removes the "no data yet" blocker at every stage of development — from UI prototyping to API testing to model training. Define your schema, configure field types, and export a complete realistic dataset in under a minute. No code, no setup, no privacy concerns.

Generate your dataset now: Jaconir Synthetic Data Factory →