How to Choose the Best Synthetic Data Generator for Databases

The difference

Same tables. Very different data.

Most generators fill each column on its own. Your application doesn't read columns on their own.

Typical fake data

Looks fine. Breaks your tests.

Each column is random on its own.

customers
id	name	city
1042	Priya Nair	Pune
1043	Lucas Meyer	Berlin
9471	no such customer

orders
id	customer_id	ordered	shipped	total
5512	9471	2026-03-14	2026-02-02	$87.10
5513	1043	2026-03-15	2026-03-17	$12.99

order_items for order 5512
product	qty	price
Desk lamp	2	$24.50

The order points at customer 9471, who is not in the customers table, so the join returns nothing.
Shipped six weeks before it was ordered.
Total is $87.10, but the items add up to $49.00.

GenerateRealData

Behaves like production.

Tables are generated together, in dependency order.

customers
id	name	city
1042	Priya Nair	Pune
1043	Lucas Meyer	Berlin

orders
id	customer_id	ordered	shipped	total
5512	1042	2026-03-14	2026-03-16	$49.00
5513	1043	2026-03-15	2026-03-17	$12.99

order_items for order 5512
product	qty	price
Desk lamp	2	$24.50

The order points at customer 1042, who exists in the customers table.
Shipped two days after the order date.
Total equals the sum of its line items.

The landscape

Four kinds of tools. One job to get right.

Every approach has a place. Here is what each is good at, and where each falls short for real databases.

01

Field-by-field online generators

Pick a type for each column, download a file.

Good for: a quick single table.

02

AI chatbots

Ask for sample rows in a conversation.

Good for: a few dozen rows to sketch an idea.

03

Code libraries

Write scripts that generate each value.

Good for: engineers with time to build and maintain it.

04

Data masking platforms

Copy production and disguise the sensitive parts.

Good for: large enterprises with access to production.

Swipe to compare →

What matters	GenerateRealData	Field-by-field generators	AI chatbots	Code libraries	Masking platforms
Starts from your existing SQL schema	Yes	Partly	Small schemas	You code it	Yes
Every foreign key resolves, across all tables	Yes	Partly	Breaks at scale	If you code it	Yes
Related columns agree (dates, totals)	Yes	Rarely	Inconsistent	If you code it	Yes
Realistic distributions, not uniform random	Yes	Partly	Partly	If you code it	Yes
Time series with trend and seasonality	Yes	Rarely	No	If you code it	Limited
Load-ready SQL for PostgreSQL, MySQL, SQL Server, SQLite	Yes	Partly	Needs fixing	You build it	Yes
Works without real production data	Yes	Yes	Yes	Yes	Needs production
Time to first dataset	Minutes	Minutes	Minutes	Hours to days	Weeks
Typical cost	Free trial, from $12	Free to low	Low	Engineering time	Enterprise contracts

Typical capabilities of each category. Individual tools vary.

The checklist

7 questions to ask before you choose

Ask these of any tool. The answers tell you whether its data will survive contact with your application.

1

Does every foreign key point at a row that exists?

Orphaned keys make joins return nothing and screens render empty, so your tests pass for the wrong reasons.

GenerateRealData: tables are generated in dependency order, so every key resolves.

2

Can it start from the schema you already have?

Rebuilding thirty tables field by field is hours of work, and it drifts from production the moment you finish.

GenerateRealData: upload your CREATE TABLE statements and your own DDL is kept.

3

Do values follow realistic distributions?

Real prices are skewed and real statuses are uneven. Uniform randomness hides the bugs that only appear at the edges.

GenerateRealData: skewed amounts, weighted categories and correlated columns.

4

Do related columns agree with each other?

A delivery before its order, or a total that doesn't match its items, fails validation and confuses reports.

GenerateRealData: date order, lookups and parent totals are enforced, not hoped for.

5

Can it produce believable time series?

Dashboards and forecasts need trend, seasonality and noise, per product or per device, not one flat line.

GenerateRealData: calendar-aware series with their own trend and seasons per entity.

6

Will the output load without edits?

Hand-fixing quotes, identity columns and insert order defeats the purpose of generating data at all.

GenerateRealData: one transaction-safe SQL file per database, plus CSV, Excel and JSON.

7

Does it need access to real production data?

Copying production into test environments is a compliance risk, even when it is masked.

GenerateRealData: never. It works from structure or a plain-English description, and uploaded data rows are stripped.

Questions

What makes a synthetic data generator good for databases?

It must keep the data consistent across tables: every foreign key points at a real row, related columns agree, and values follow realistic distributions. Single-table realism is not enough for testing a real application.

Can I just ask an AI chatbot for test data?

For a few dozen rows, often yes. Beyond that, chatbots tend to repeat values, break relationships between tables and drift from your schema. GenerateRealData uses AI for context and a deterministic engine for the rows, so consistency holds at any size.

Do I need real production data to get realistic synthetic data?

No. GenerateRealData works from your schema's structure or a plain-English description. It never needs real rows, and any data rows in an uploaded file are removed before processing.

Is there a free way to try it?

Yes. Every new account gets a free trial, with no credit card required. Paid plans start at $12 a month.

Not all synthetic data is equal. Here's how to choose.