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Mock SQL Data Generator

Generate realistic fake test data from a SQL schema and emit INSERT statements, CSV, or JSON. Parse CREATE TABLE DDL or define fields manually; per-column data types include names, emails, dates, numbers, addresses, UUIDs, IPs, ENUMs and regex patterns. FK-aware relational generation (child rows reference valid parent keys), deterministic seed for reproducible data, blank %, unique constraints, batched INSERTs, and per-dialect output (MySQL, PostgreSQL, SQLite, SQL Server). Unlimited rows, 100% client-side, no server.

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Dialect

Rows / table

Seed

INSERT batch size

Preset DDL

1 tables20 rows0 FKsseed: 42

CREATE TABLE DDL

The tool parses columns, types, NOT NULL, defaults, ENUM values, inline REFERENCES, table-level FOREIGN KEY clauses, PRIMARY KEY and UNIQUE constraints. Inline REFERENCES are promoted to FK relationships automatically.

Parsed tables (1)

users

7 columns · 0 FK · 1 unique

id: bigintemail: varcharfirst_name: varcharlast_name: varcharage: tinyintis_active: boolean+1 more

Output

-- Mock data for table: users (20 rows)
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'gary.nelson8524@mock.io', 'Charles', 'Gomez', 69, 1, '2026-07-24 15:12:41');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'betty.wood7457@test.org', 'Christopher', 'Mitchell', 68, 1, '2026-07-24 15:10:28');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'debra.jones5923@example.com', 'Margaret', 'Garcia', 82, 1, '2026-07-24 04:31:36');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'sarah.watson4877@fake.net', 'Kimberly', 'Nelson', NULL, 1, '2026-07-24 17:14:03');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'gary.white6457@example.com', 'Ashley', 'Peterson', 59, 1, '2026-07-24 10:41:48');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'donald.davis6598@mock.io', 'Larry', 'Ruiz', 95, 1, '2026-07-25 02:29:27');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'joseph.hernandez159@test.org', 'Ronald', 'Collins', 24, 1, '2026-07-25 02:18:45');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'mark.ortiz1797@mock.io', 'Christine', 'Lopez', 64, 1, '2026-07-24 14:17:30');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'jacob.allen5962@test.org', 'Pamela', 'Lewis', 87, 1, '2026-07-25 03:43:55');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'patrick.adams5452@test.org', 'William', 'Gutierrez', 82, 1, '2026-07-24 06:07:23');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'deborah.ward1300@fake.net', 'Charles', 'Ortiz', 36, 1, '2026-07-24 07:47:54');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'jonathan.young2070@mock.io', 'Patrick', 'Young', 50, 1, '2026-07-25 00:36:17');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'joshua.smith7374@mock.io', 'Catherine', 'Gray', 67, 1, '2026-07-24 08:17:32');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'john.ortiz7083@test.org', 'Mark', 'Young', NULL, 1, '2026-07-25 03:43:13');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'cynthia.sanders1639@example.com', 'Ryan', 'Ramirez', 53, 1, '2026-07-24 20:20:35');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'carol.young8956@test.org', 'Stephanie', 'Collins', 66, 1, '2026-07-24 11:41:04');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'ashley.walker6693@example.com', 'Sarah', 'Gutierrez', 28, 1, '2026-07-24 04:53:37');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'jacob.martin4815@test.org', 'Justin', 'Gray', 54, 1, '2026-07-25 00:41:56');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'jason.richardson8945@test.org', 'Jessica', 'Collins', 35, 1, '2026-07-24 16:58:18');
INSERT INTO `users` (`id`, `email`, `first_name`, `last_name`, `age`, `is_active`, `created_at`) VALUES (1, 'aaron.campbell8594@fake.net', 'Nicole', 'Garcia', 31, 1, '2026-07-24 20:06:56');

Faker type reference (36)

Column types are inferred automatically from the SQL type and column name (e.g. email → email, price → money, created_at → timestamp). To override, manually edit the parsed schema (advanced — not exposed in this UI; consider using the CREATE TABLE Generator tool to design schemas with explicit faker hints).

auto— Pick a sensible faker type from the SQL column type.

first_name— e.g. Alice

last_name— e.g. Smith

full_name— e.g. Alice Smith

email— e.g. alice.smith@example.com

username— e.g. alice.smith42

phone— e.g. +1-555-123-4567

street— e.g. 123 Main St

city— e.g. Springfield

state— e.g. IL

country— e.g. United States

zip— e.g. 62704

company— e.g. Acme Inc

job_title— e.g. Software Engineer

sentence— Lorem-ipsum short sentence

paragraph— Lorem-ipsum paragraph

url— e.g. https://example.com/page

ipv4— e.g. 192.168.1.1

ipv6— e.g. 2001:db8::1

uuid— e.g. 550e8400-e29b-41d4-a716-446655440000

date_past— ISO date in the past 30 years

date_future— ISO date in the next 5 years

date_recent— ISO date in the last 90 days

timestamp— ISO timestamp (datetime)

time— HH:MM:SS

boolean— true/false (or 0/1)

int— Random int between min/max (default 1-1000)

bigint— Random bigint between min/max

decimal— Random decimal with scale

money— Currency value e.g. 1234.56

age— Integer 18-99

hex_color— e.g. #1a2b3c

enum— Pick from enumValues with optional weights

regex— String matching the `pattern` regex

constant— Always emit `constantValue`

null— Emit NULL (only for nullable columns)

Privacy: All mock data generation runs locally. Your DDL and generated data never leave the browser. History is stored in localStorage on this device only.

About Mock SQL Data Generator

How to use

Enter your input in the tool above.
Adjust any options to your preference.
Use the Copy or Download buttons to save the result.
Everything happens locally — your data never leaves your browser.

FAQ

How does the Mock SQL Data Generator produce data?

Paste a CREATE TABLE statement (the tool parses columns, types, NOT NULL, defaults, ENUM values and inline foreign-key references) or define fields manually with a faker type per column. The generator then produces N rows of realistic mock data — names, emails, dates, numbers, addresses, UUIDs, IPs, ENUMs, regex-matched strings — and emits INSERT statements, CSV, or JSON. All generation is deterministic given the same seed.

Does it handle foreign-key relationships?

Yes. If your DDL declares inline REFERENCES clauses (or you define FK relationships manually), the generator produces parent tables first, collects the generated parent-key values, and then generates child rows whose FK columns reference one of those valid parent keys — preserving referential integrity. Composite foreign keys are supported, and circular dependencies are detected and reported.

Is the generated data reproducible?

Yes. The tool uses a deterministic, seedable PRNG (mulberry32). Enter the same seed and you get the identical dataset every time — useful for sharing test cases or for golden-file CI checks. Switch the seed to get a fresh dataset for the same schema.

What data types are supported per column?

30+ faker types: first_name, last_name, full_name, email, username, phone, street, city, state, country, zip, company, job_title, sentence, paragraph, url, ipv4, ipv6, uuid, date_past, date_future, date_recent, timestamp, boolean, int (with min/max), decimal (with precision), bigint, money, age, hex_color, enum (with weighted values), regex (with pattern), and constant. Each type produces type-appropriate realistic values.

What extras does this tool offer versus Mockaroo and others?

(1) Unlimited rows (no 1,000-row cap). (2) Fully offline — your schema never leaves the browser. (3) FK-aware relational generation. (4) Deterministic seed. (5) 30+ faker types. (6) Weighted ENUMs. (7) Regex-pattern column. (8) Blank % per column. (9) Unique constraint enforcement (collision-safe). (10) Batched INSERTs (configurable batch size). (11) Multi-dialect output (MySQL, PostgreSQL, SQLite, SQL Server). (12) CSV + JSON output. (13) Parse DDL or define fields manually. (14) localStorage history (max 20). (15) Shareable URL. (16) Copy / download .sql / .csv / .json.

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All tools

Mock SQL Data Generator

CREATE TABLE DDL

Parsed tables (1)

Output

Faker type reference (36)

About Mock SQL Data Generator

How to use

FAQ

How does the Mock SQL Data Generator produce data?

Does it handle foreign-key relationships?

Is the generated data reproducible?

What data types are supported per column?

What extras does this tool offer versus Mockaroo and others?

Related tools

.bashrc / .zshrc Alias & Config Manager

Add / Subtract Date Calculator

Age Calculator

Search tools and actions

CREATE TABLE DDL

Parsed tables (1)

Output

Faker type reference (36)