How to Generate Test Data for PostgreSQL (2 Methods)

Need realistic test data for your PostgreSQL database? Here are two approaches — pick the one that fits your workflow.

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Generating test data for PostgreSQL shouldn't require copying production data or writing custom Faker scripts. Whether you need a quick dataset for a demo or automated data generation in your CI/CD pipeline, there's a faster way.

DDL to Data generates realistic test data directly from your PostgreSQL schema. Just paste your CREATE TABLE statements and get back JSON, CSV, or SQL INSERT statements instantly.

Two Ways to Generate PostgreSQL Test Data

1. Dashboard (No Code Required)

Best for: Quick demos, manual testing, non-technical users

Generate PostgreSQL Test Data — No Code Guide →

The visual dashboard lets you:

• Paste your PostgreSQL DDL and get data immediately
• Download as JSON, CSV, or SQL
• Store schemas for reuse
• No API key required for basic usage

Perfect when you need data now and don't want to write any code.

2. REST API (For Automation)

Best for: CI/CD pipelines, automated testing, developer workflows

PostgreSQL Test Data API for CI/CD →

The REST API enables:

• Programmatic data generation
• Integration with GitHub Actions, CircleCI, Jenkins
• Database seeding in your test setup
• Consistent, repeatable test data

Ideal for teams who want test data generation baked into their development workflow.

How It Works

DDL to Data reads your PostgreSQL schema and infers realistic values from column names:

CREATE TABLE customers (
  id SERIAL PRIMARY KEY,
  first_name VARCHAR(50),
  last_name VARCHAR(50),
  email VARCHAR(100) UNIQUE,
  phone VARCHAR(20),
  company_name VARCHAR(100),
  created_at TIMESTAMP DEFAULT NOW()
);

Automatically generates:

[
  {
    "id": 1,
    "first_name": "Sarah",
    "last_name": "Chen",
    "email": "[e-mail]",
    "phone": "[phone]",
    "company_name": "Nexus Industries",
    "created_at": "2024-03-15T09:23:41"
  },
  {
    "id": 2,
    "first_name": "Marcus",
    "last_name": "Johnson",
    "email": "[e-mail]",
    "phone": "[phone]",
    "company_name": "Velocity Labs",
    "created_at": "2024-03-14T14:56:12"
  }
]

No configuration needed. Column names like email, phone, first_name, and company_name are automatically recognized and populated with appropriate data.

Why Not Just Use Production Data?

Copying production data into dev environments creates problems:

• Security risk — PII in non-production environments
• Compliance issues — GDPR, HIPAA, SOC 2 violations
• DevOps overhead — VPC peering, access controls, data masking
• Stale data — Production copies get outdated

DDL to Data generates production-shaped data without any of these concerns. The data looks real but isn't tied to real people.

Why Not Use Faker Directly?

Faker libraries work, but require:

• Mapping every column to the right generator
• Writing and maintaining seed scripts
• Updating scripts when schemas change
• Wiring up foreign key relationships manually

DDL to Data handles this automatically. Your schema already defines the structure — we just read it.

PostgreSQL-Specific Features

Data Type Support

Full support for PostgreSQL types:

• SERIAL, BIGSERIAL, SMALLSERIAL
• VARCHAR, TEXT, CHAR
• INTEGER, BIGINT, SMALLINT
• NUMERIC, DECIMAL, REAL, DOUBLE PRECISION
• BOOLEAN
• DATE, TIME, TIMESTAMP, TIMESTAMPTZ
• UUID
• JSON, JSONB

Foreign Key Relationships

Define relationships and get referentially-correct data:

CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  customer_id INTEGER REFERENCES customers(id),
  total DECIMAL(10,2),
  status VARCHAR(20)
);

The generated customer_id values will match valid id values from the customers table.

Constraints Respected

• NOT NULL — Always populated
• UNIQUE — No duplicate values
• CHECK — Constraints honored where possible

Get Started

Try the dashboard: ddltodata.com — paste your schema, get data

Use the API: Get your free API key — 50,000 rows/month free

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Next: Learn the specific steps for no-code dashboard usage or API integration.