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Best SQL Test Data Generator Tools in 2026

August 17, 2026 by Chat2DB Team

Every database project hits the same wall: you have a schema, but no data. Empty tables make it impossible to test queries, exercise pagination, validate constraints, or demo a feature. Copying production data is the tempting shortcut, and it is also the one most likely to get you into legal trouble. What you actually need is a good test data generator.

This guide ranks the SQL test data generator tools worth using in 2026 — web apps, code libraries, commercial suites, and plain SQL tricks — with honest notes on what each is good at and where it falls short.

1. Chat2DB SQL Test Data Generator (Free, Browser-Based)

The Chat2DB SQL Test Data Generator (opens in a new tab) is a free online dummy data generator that runs entirely in your browser. You define columns and their types (names, emails, dates, numbers, and other realistic field kinds), set a row count, and it produces ready-to-run SQL INSERT statements you can paste straight into your database client.

Good at:

• Zero friction: no signup, no install, no configuration files. Open the page, describe the table, copy the SQL.
• Privacy by design: generation happens in the browser, so your schema and generated data never leave your machine — relevant if your table names themselves are sensitive.
• Output is plain INSERT statements, which work with any SQL database and fit naturally into seed scripts and code review.

Limitations:

• It is a focused single-table tool; for multi-table datasets with foreign-key chains you will combine it with the SQL techniques covered later in this article.
• Browser-based generation is best suited to seed-sized datasets (hundreds to tens of thousands of rows), not benchmark-scale volumes.

If you want to go beyond one-off generation, the Chat2DB desktop client (opens in a new tab) and the web version (opens in a new tab) add AI-assisted SQL: you can describe the data you need in natural language, generate and refine INSERT statements against your real schema, and run them in the same tool.

2. Mockaroo

Mockaroo (opens in a new tab) is the long-standing web-based workhorse of test data generation.

Good at:

• A very large catalog of field types: names, addresses, geo coordinates, product names, regex-based custom formats, weighted distributions, and formula fields that reference other columns.
• Multiple output formats — SQL, CSV, JSON, Excel — plus saved schemas and a REST API so CI jobs can fetch fresh data on demand.
• Percent-blank settings per field, which is an easy way to test how your app handles NULL.

Limitations:

• The free tier caps rows per download; larger datasets and API volume require a paid plan.
• Data is generated on Mockaroo's servers, so schema definitions leave your machine — a policy problem in some organizations.
• Cross-table referential integrity is possible but manual and fiddly.

3. Faker Libraries (faker-js, Python Faker)

When generation logic needs to live in your codebase, use a Faker library: @faker-js/faker for TypeScript/JavaScript or Faker for Python. Because it is code, you get unlimited rows, version control, and full control over distributions and edge cases.

Here is a complete TypeScript script that generates SQL INSERT statements for a customers table:

// generate-customers.ts
// npm install @faker-js/faker
import { faker } from "@faker-js/faker";
import { writeFileSync } from "node:fs";
 
const ROWS = 1000;
 
// Escape single quotes for SQL string literals
const q = (s: string): string => `'${s.replace(/'/g, "''")}'`;
 
const statements: string[] = [];
 
for (let i = 1; i <= ROWS; i++) {
  const firstName = faker.person.firstName();
  const lastName = faker.person.lastName();
  // Derive the email from the name so rows look coherent, not random
  const email = faker.internet
    .email({ firstName, lastName, provider: "example.com" })
    .toLowerCase();
  const city = faker.location.city();
  const signupDate = faker.date
    .between({ from: "2024-01-01", to: "2026-08-01" })
    .toISOString()
    .slice(0, 10);
  const lifetimeValue = faker.number.float({ min: 0, max: 5000, fractionDigits: 2 });
 
  statements.push(
    `INSERT INTO customers (id, first_name, last_name, email, city, signup_date, lifetime_value) ` +
      `VALUES (${i}, ${q(firstName)}, ${q(lastName)}, ${q(email)}, ${q(city)}, ` +
      `'${signupDate}', ${lifetimeValue});`
  );
}
 
writeFileSync("customers_seed.sql", statements.join("\n"));
console.log(`Wrote ${ROWS} INSERT statements to customers_seed.sql`);

Step by step: the script escapes quotes (never skip this, even for fake data), derives the email from the generated name so the row reads as a plausible person, constrains dates to a realistic signup window, and writes everything to a .sql file you can load with psql -f customers_seed.sql or any SQL client.

Good at: repeatable seeds (faker.seed(42) gives deterministic output), locale-specific data, and living next to your tests.

Limitations: you write and maintain the code, including uniqueness handling (dedupe emails yourself) and batching for very large volumes — one million single-row INSERTs is slow; batch values or generate CSV for bulk COPY instead.

4. generatedata.com

generatedata.com (opens in a new tab) is an open-source data generator you can use hosted or self-host from the GitHub project.

Good at: self-hosting inside a private network (solves the "data leaves the building" objection to hosted tools), a decent set of country-specific field types, and SQL/CSV/JSON/XML export.

Limitations: the field-type catalog and UI polish trail Mockaroo, and hosted use has row limits. Development activity has historically come in bursts, so evaluate the current state before standardizing on it.

5. dbForge Data Generator

Devart's dbForge Data Generator is a commercial desktop tool, strongest in the SQL Server ecosystem (with a MySQL/Oracle sibling in dbForge Studio editions).

Good at:

• Schema-aware generation: it reads your real database, understands check constraints, identity columns, and — crucially — foreign keys, generating parent and child tables in the correct order with valid references.
• Large libraries of predefined, domain-specific generators and a preview grid before anything is written.
• Command-line support so generation can run in scheduled jobs.

Limitations: commercial licensing, Windows-centric tooling, and overkill if you just need a few hundred rows for a unit test.

6. pgbench and sysbench

These are benchmark tools, not general data generators, but they are the right answer to one specific question: "I need millions of rows to test performance, and I do not care what the data says."

-- pgbench ships with PostgreSQL. Initialize a scale-100 dataset
-- (~10 million rows in pgbench_accounts):
--   pgbench -i -s 100 mydatabase
-- Then run a throughput test:
--   pgbench -c 10 -j 2 -T 60 mydatabase

Good at: generating bulk volume fast, with standardized schemas (TPC-B-like for pgbench, OLTP tables for sysbench) that make performance runs comparable over time.

Limitations: fixed schemas and meaningless values — you cannot use pgbench to populate your tables, and the data has no realistic distribution. Use them for load testing, not application seeding.

7. Plain SQL: generate_series Tricks

Sometimes the best test data generator is the database itself. PostgreSQL's generate_series plus random() can populate your own tables with zero external tools. Here is a full worked example:

-- 1. Target table
CREATE TABLE employees (
    id         integer PRIMARY KEY,
    full_name  text        NOT NULL,
    department text        NOT NULL,
    hired_on   date        NOT NULL,
    salary     numeric(10,2) NOT NULL
);
 
-- 2. Generate 50,000 rows of plausible data
INSERT INTO employees (id, full_name, department, hired_on, salary)
SELECT
    gs.id,
    -- Random name: pick from small arrays of first and last names
    (ARRAY['Alice','Bruno','Chen','Dara','Elena','Farid','Grace','Hiro',
           'Ines','Jonas'])[1 + floor(random() * 10)::int]
    || ' ' ||
    (ARRAY['Almeida','Berg','Costa','Dubois','Eriksen','Fischer','Garcia',
           'Haddad','Ito','Jansen'])[1 + floor(random() * 10)::int],
    -- Weighted department choice: engineering appears twice, so it is picked more often
    (ARRAY['Engineering','Engineering','Sales','Marketing','Support',
           'Finance'])[1 + floor(random() * 6)::int],
    -- Random hire date within the last 8 years
    CURRENT_DATE - (floor(random() * 365 * 8))::int,
    -- Salary in the 40,000 to 140,000 range, rounded to cents
    round((40000 + random() * 100000)::numeric, 2)
FROM generate_series(1, 50000) AS gs(id);
 
-- 3. Sanity-check the distribution
SELECT department, count(*), round(avg(salary), 2) AS avg_salary
FROM employees
GROUP BY department
ORDER BY count(*) DESC;

How it works: generate_series(1, 50000) produces one row per id; array indexing with 1 + floor(random() * n)::int picks a random element (Postgres arrays are 1-based, hence the + 1); repeating a value in the array is a cheap weighting trick; and date arithmetic with CURRENT_DATE - n spreads hire dates over eight years. This approach is fast because everything happens inside the database in a single statement.

Good at: speed, zero dependencies, and full control from within migrations or CI scripts. MySQL 8+ can approximate this with recursive CTEs.

Limitations: realism is limited to what you hand-encode — ten first names will not exercise collation edge cases or Unicode handling the way a Faker locale will.

Generating Foreign-Key-Consistent Datasets

Single tables are easy; the hard part of test data is referential integrity. Three practical rules:

1. Generate parents first, then sample their keys for children. In SQL, the cleanest pattern is to select from the parent table when inserting children:

-- Every order references a real employee as its owner:
CREATE TABLE orders (
    id          serial PRIMARY KEY,
    employee_id integer NOT NULL REFERENCES employees(id),
    amount      numeric(10,2) NOT NULL,
    ordered_on  date NOT NULL
);
 
INSERT INTO orders (employee_id, amount, ordered_on)
SELECT
    -- Sample a real parent key: random id in the known 1..50000 range
    1 + floor(random() * 50000)::int,
    round((10 + random() * 990)::numeric, 2),
    CURRENT_DATE - (floor(random() * 365))::int
FROM generate_series(1, 200000);

2. Skew the distribution. Real foreign keys follow power laws — a few customers place most orders. Sampling with floor(power(random(), 3) * 50000)::int + 1 concentrates references on low ids, which surfaces the index and join behavior your uniform data would hide.
3. In code, keep generated keys in memory. With Faker, push parent ids into an array as you create them and have child rows draw from that array, rather than guessing id ranges.

Tools that read your schema (dbForge, and Chat2DB's AI-assisted generation working against a live connection) can infer these relationships for you; browser generators generally cannot, so plan the insert order yourself.

Do Not Use Production Data

The reason test data generators exist is that the alternative — copying production — is a liability. Under GDPR, personal data copied into a staging database is still personal data: it needs the same access controls, retention rules, and breach reporting as production, and developer laptops rarely provide any of that. Similar duties arise under CCPA, HIPAA, and most modern privacy regimes.

If you genuinely need production-shaped data, mask it: replace names, emails, phone numbers, and free-text fields with generated values while preserving row counts, null rates, and value distributions. Do the masking inside the production environment before the copy leaves it, and treat any keys or lookup tables used for masking as secrets. For everything else, synthetic data from the tools above is safer, cheaper to govern, and — because you control the distributions — often better at exposing edge cases than a production snapshot would be.

How to Choose

• A few hundred rows for one table, right now: the Chat2DB online generator — fastest path from schema to INSERT statements, nothing leaves the browser.
• Rich multi-format datasets with an API: Mockaroo, if hosted generation is acceptable; generatedata.com self-hosted if it is not.
• Data generation as versioned code in your test suite: a Faker library.
• Schema-aware, FK-correct enterprise seeding (especially SQL Server): dbForge Data Generator.
• Millions of rows for performance testing: pgbench or sysbench for standard benchmarks; generate_series when the rows must land in your own schema.

Most teams end up with two of these: a code-based generator wired into tests, and a quick interactive tool for ad-hoc work. Start there, and add heavier tooling only when referential complexity or volume demands it.