Free Synthetic Database Generator

01 · Definition

What is synthetic database generator?

A database generator creates synthetic schema-adjacent values for tests, demos and fixture files. It is useful when developer tools need realistic database field shapes without copying production table names, database settings or stored identifiers into lower-control environments.

The tool above sends your count, locale, seed and method to the Spotzee Extended API, then renders returned databases[] records. Use profile for expanded records, or choose one method such as collation, column, engine, mongodbObjectId or type. Read the in-depth database generator guide for endpoint options, response fields and fixture design notes.

02 · Process

How it works

1. 1Enter a count.Type the number of database records to generate, from 1 to 100.
2. 2Choose a method.Pick profile for expanded database data, or choose one field method such as column, engine, mongodbObjectId or type.
3. 3Set repeatability.Use a locale where supported and a numeric seed when the same database test data generator output must repeat.
4. 4Generate records.The server validates the count and seed, forwards a form-encoded request to /generic/data/generate/database and renders the returned records.

03 · Risk

Why it matters

Database examples sit in docs, migration tests, import previews and developer tools. They need believable columns, engines, collations, ObjectId strings and data types, but they should not expose real schema names or production identifiers.

Synthetic database data gives engineering and QA teams controlled variation. A numeric seed lets you reproduce the same records after a failed test, while method-specific output keeps fixtures focused on the field shape under test.

04 · Use cases

Common ways to use this tool

• Schema documentation. Use it as a database generator to populate example tables, API docs and migration notes.
• Migration test fixtures. Use the database test data generator mode to create columns, types, collations and engines for parser tests.
• ObjectId parser checks. Use the MongoDB ObjectId generator method when ObjectId-shaped strings need to pass through validators.
• Column-name samples. Use the database column generator method to create field names for CSV previews, mapping screens and seed scripts.
• Repeatable developer datasets. Set a numeric seed so the same dummy data generator output can be reproduced after failures.

05 · Interpretation

What to check in the result

• Count outside 1 to 100. The API accepts up to 100 database records per request. For larger datasets, call the endpoint multiple times.
• Method not matching the field under test. Use profile for related fields together, or choose a specific method when a parser expects one value shape.
• Production names in fixtures. Replace real table, column and identifier examples with synthetic output before sharing docs or support samples.
• ObjectId assumptions. Generated MongoDB ObjectId values are fixture strings. Do not treat them as existing records in a live database.
• Unseeded snapshots. Set a numeric seed when tests assert exact strings or snapshot output.