Realistic data generator
Realistic means it behaves like it.
Realistic data is data whose patterns match how the real process behaves: weekly and seasonal rhythms, a long tail of small customers and a few large ones, prices that cluster at real price points, a machine that wears and is repaired. Misata generates relational data to those patterns and the outcomes you declare, then audits the delivered rows for consistency and realism and tells you what it found.
What usually goes wrong
Realistic data is what a demo, a training set, a BI course or a benchmark needs, where an analyst will look at the charts and the numbers must not feel invented.
Values that are valid but wrong
Every price is a random number with cents, every customer buys the same amount, every month looks the same.
No cause behind the effect
Churn, fraud or failures sprinkled at random, unrelated to anything else in the data.
Learned from real data
Models trained on production can memorise it, and you still need the production data to start.
What you get instead
Patterns by design
Seasonality, skew, correlations and state changes declared in the specification, not hoped for.
Causes you can find
Ground-truth columns record why something happened, so an analysis can be checked against the answer.
Audited for realism
Delivered rows are checked for implausible values and inconsistencies, and the findings are listed.
A request that works
A subscription business over two years: signups with a January spike, plans with real price points, churn that rises after a price change in March, and support tickets that predict churn.
Four ways to get it
In your browser
Describe it in Misata Studio, shape it, and export. Two complete builds a month are free.
Try this promptFrom your AI assistant
Connect the MCP server to Claude, Cursor or VS Code and ask. No key needed to start.
Connect the MCP serverDownload one now
Twelve free multi-table sample databases, and premium ones with the ground truth inside.
Browse datasetsIn Python
The open-source library: pip install misata, MIT licensed, runs offline.
Read the docsHow the approaches compare
| Random-value library | Form-based generator | Asking a chatbot for rows | Misata | |
|---|---|---|---|---|
| Realistic single values (names, emails, addresses) | ||||
| Several tables whose keys always join | ||||
| Declared totals and rates come out exact | ||||
| Same rows every run (seeded) | ||||
| Shows a check of what it produced | ||||
| No code needed |
General approaches, not specific products; individual tools vary. A dash means some tools do it, or it takes work.
Questions
How do you make synthetic data realistic?
Start from how the real process behaves rather than from random values: who the entities are, how they relate, what rhythms and skews the numbers follow, and what causes the events you care about. Misata takes those as a specification, generates rows that follow them, and audits the result.
Where can I get realistic sample data now?
The free sample databases at misata.studio/datasets are generated this way and profiled column by column. The premium datasets go further and include the ground truth, such as the true cause of each claim denial or each control-chart signal.
Is any of it real data?
No. Every row is generated from a specification. Nothing is copied from, sampled from or trained on a real dataset, so there is no personal data in it and nothing to scrub.
Is it free?
The twelve sample databases are free to download with no signup, under CC0. Misata Studio gives every account 20 credits a month, which is two complete builds, with no card. The MCP server needs no key or account for datasets the assistant designs itself, and the Python library is open source under MIT.