Generate realistic, FK-aware test data for any table — for development, testing, and demos

Data Generator

Generate realistic test data for your tables without leaving data-peek — no seed scripts, no Faker boilerplate, no hand-writing a thousand INSERTs.

It’s the fastest way to fill an empty dev database, stress-test a query against realistic volume, or build a believable demo dataset. Because it’s foreign-key aware, you can populate a whole related schema (organizations → users → invoices) and the rows actually join up.

Opening the Generator

Right-click a table in the sidebar and select Generate Data, or open it from the command palette. It opens as a dedicated tab.

Configuring Columns

For each column, choose a generator:

Generator	Output
Auto-increment	Sequential integers
UUID	Random UUIDs
Faker: name	Realistic names
Faker: email	Realistic emails
Faker: phone	Phone numbers
Random integer	Integer within a range
Random float	Float within a range
Random boolean	true/false
Random date	Date within a range
Random enum	Random pick from a list
FK reference	Real values from a referenced table
Fixed value	A constant value for all rows
NULL	Always NULL
SQL expression	Custom SQL (e.g. `NOW()`)

Generation Options

Row count — generate 1 to 10,000 rows
Seed — set a seed for reproducible output

Preview Mode

Click Preview to generate sample rows without inserting them. Review the data, adjust generators, then click Generate to insert.

Progress

A progress indicator shows generation status. You can cancel at any time.

To fill organizations then users so every user belongs to a real org:

On organizations, set name → Faker: name, plan → Random enum (free, pro, enterprise), generate 20 rows.
On users, set email → Faker: email, organization_id → FK reference (organizations.id), generate 500 rows.

Now the join works out of the box:

SELECT o.plan, count(u.id) AS users
FROM organizations o JOIN users u ON u.organization_id = o.id
GROUP BY o.plan;

Use the FK reference generator to maintain referential integrity — it pulls real values from the parent table, so generate parents before children. Set a seed when you need the same dataset every run (handy for reproducible tests or screenshots).

CSV Import — load real data from a file instead of generating it
Inline Editing — tweak individual generated rows
Table Designer — create the table first, then fill it

Data Generator

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