Upload your schema. Get a working database.

Paste or upload your DDL and get back data that fits it exactly: your table and column names, your types, your keys and your CHECK constraints.

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The example

A SaaS billing schema, loaded into MySQL

What to look for in the screenshot above. These are the details that usually break in hand-made or random test data.

Six tables from an uploaded schema: accounts, users, subscriptions, invoices, feature_flags and audit_log.
Monthly recurring revenue is exactly seats × price_per_seat, and every invoice amount matches it.
Every renews_on date falls after started_on, as the schema's CHECK constraint requires.
Invoice statuses follow a realistic mix: mostly paid, some open, a few past due or void.

Table names, column names, types, defaults and indexes come from your file, not from a guess.

Primary keys are unique, foreign keys resolve, NOT NULL columns are filled and CHECK rules hold.

Output for PostgreSQL, MySQL / MariaDB, SQL Server and SQLite, loaded with one command.

We read your schema, never your rows. Data statements in an uploaded file are removed before processing.

How it works

Describe your data in plain English, or upload your SQL schema for PostgreSQL, MySQL / MariaDB, SQL Server and SQLite.

Check the tables, columns and rules the AI proposes, and adjust anything you like.

Choose how many rows you need and download a ready-to-run SQL script, CSV, Excel (one sheet per table) or JSON.

Which files can I upload?

Any file of CREATE TABLE statements, including schema-only dumps from pg_dump, mysqldump or SQL Server scripts.

What if my schema has hundreds of columns?

That's fine. Each column gets a generator that fits its name, type and constraints.

Can I add rules the DDL can't express?

Yes. Add plain-English notes, like 'renewal is 12 months after start', and review the plan before generating.