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Generate PostgreSQL Test Data Without Code (Step-by-Step)

#postgres #nocode #tutorial #datascience

Need PostgreSQL test data fast? Here's how to generate it in under a minute — no coding required.

Whether you're preparing a demo, testing a feature, or onboarding to a new project, you need realistic data. But copying production data is a security nightmare, and writing Faker scripts takes time you don't have.

DDL to Data's visual dashboard generates realistic PostgreSQL test data instantly. Just paste your schema and download the results.

Step 1: Go to the Dashboard

Visit ddltodata.com and scroll to the interactive demo.

You'll see a code editor pre-filled with an example schema. You can use this to test, or paste your own.

Step 2: Paste Your PostgreSQL Schema

Replace the example with your CREATE TABLE statement:

CREATE TABLE products (
  id SERIAL PRIMARY KEY,
  name VARCHAR(100) NOT NULL,
  description TEXT,
  price DECIMAL(10,2),
  category VARCHAR(50),
  sku VARCHAR(20) UNIQUE,
  in_stock BOOLEAN DEFAULT true,
  created_at TIMESTAMP DEFAULT NOW()
);
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The dashboard accepts standard PostgreSQL DDL syntax including:

  • Multiple CREATE TABLE statements
  • Foreign key relationships
  • Constraints (NOT NULL, UNIQUE, DEFAULT)
  • All common PostgreSQL data types

Step 3: Click "Generate Test Data"

Hit the generate button. In milliseconds, you'll see realistic data:

[
  {
    "id": 1,
    "name": "Wireless Bluetooth Headphones",
    "description": "Premium noise-canceling headphones with 30-hour battery life",
    "price": 149.99,
    "category": "Electronics",
    "sku": "WBH-2024-001",
    "in_stock": true,
    "created_at": "2024-03-15T09:23:41"
  },
  {
    "id": 2,
    "name": "Ergonomic Office Chair",
    "description": "Adjustable lumbar support with breathable mesh back",
    "price": 299.00,
    "category": "Furniture",
    "sku": "EOC-2024-042",
    "in_stock": true,
    "created_at": "2024-03-14T14:56:12"
  }
]
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Notice how:

  • name contains realistic product names
  • description has plausible product descriptions
  • price is a reasonable decimal value
  • sku follows a realistic SKU format
  • category contains appropriate category names

All inferred automatically from column names.

Step 4: Choose Your Format

Use the format toggle to switch between:

  • JSON — For APIs, JavaScript, most modern apps
  • CSV — For spreadsheets, data imports, Excel
  • SQL — Ready-to-run INSERT statements for PostgreSQL

SQL Output Example 

INSERT INTO products (id, name, description, price, category, sku, in_stock, created_at) VALUES
(1, 'Wireless Bluetooth Headphones', 'Premium noise-canceling headphones with 30-hour battery life', 149.99, 'Electronics', 'WBH-2024-001', true, '2024-03-15T09:23:41'),
(2, 'Ergonomic Office Chair', 'Adjustable lumbar support with breathable mesh back', 299.00, 'Furniture', 'EOC-2024-042', true, '2024-03-14T14:56:12');
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Copy and paste directly into psql or your PostgreSQL client.

Step 5: Download or Copy

  • Copy — Click to copy to clipboard
  • Download — Save as a file

That's it. Realistic test data in under 60 seconds.

Working with Multiple Tables

The dashboard handles multi-table schemas with foreign keys:

CREATE TABLE categories (
  id SERIAL PRIMARY KEY,
  name VARCHAR(50) NOT NULL
);

CREATE TABLE products (
  id SERIAL PRIMARY KEY,
  category_id INTEGER REFERENCES categories(id),
  name VARCHAR(100) NOT NULL,
  price DECIMAL(10,2)
);
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The generated data maintains referential integrity — every category_id in products will reference a valid id from categories.

Save Your Schemas (Optional)

Create a free account to:

  • Store schemas — Access them anytime without re-pasting
  • Generate more data — 50,000 rows/month free
  • Get an API key — For automation (see API guide)

Common Use Cases

Demo Preparation

Need to show a client realistic data? Generate it in seconds instead of sanitizing production data or hand-crafting fake records.

QA Testing

Fill your test database with varied, realistic data. Catch edge cases that test@test.com and John Doe would never reveal.

Developer Onboarding

New team members can seed their local database immediately without production access or waiting for data dumps.

Staging Environment Refresh

Repopulate staging with fresh data for each release cycle. No DevOps tickets, no data masking, no security review.

Tips for Better Results

Use Descriptive Column Names

The more descriptive your column names, the better the generated data:

Good column names → realistic data:

Vague column names → random strings:

  • e → xK9mP2qL
  • ph → 7nR4tY8w
  • cn → Lm3pQ6vX

Include Data Types

Explicit types help generate appropriate values:

-- Better
price DECIMAL(10,2)

-- Works but less precise
price VARCHAR(20)
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Add Foreign Keys

Relationships ensure referential integrity:

-- Explicit relationship
user_id INTEGER REFERENCES users(id)

-- Also works (inferred from naming)
user_id INTEGER
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What's Next?

Generate your first dataset now at ddltodata.com — no signup required.

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