For data engineering
Test data that actually
tests something.
A test database full of rows that pass a schema check but fail the first real JOIN is worse than an empty one, because it looks like coverage. Misata generates relational data from your own schema, verifies the relationships actually hold, and reproduces the exact same dataset on demand.
Where the usual approaches break
Copying production
Drags every compliance obligation the real rows carried into an environment with weaker controls, and someone has to own that risk.
A hand-written seed script
Rots the first time the schema migrates, and nobody notices until a test starts failing for the wrong reason.
Faker in a loop
Fills each column independently. Foreign keys point at rows that don't exist, so anything downstream of a JOIN is untested by accident.
Static fixture files
Work once, then someone edits them by hand for a new test case and the relationships between tables quietly stop matching.
What Misata does instead
Reads your actual schema
Point it at a dev database or a schema file. Tables, columns, and foreign keys come from the source itself, so it stays correct as the schema changes.
Referential integrity, checked, not assumed
Parents insert before children, and Misata queries the database afterward to confirm zero orphans, not just that it tried.
Deterministic from a seed
The same schema and seed produce byte-identical output. A test that failed on Tuesday's data can be reproduced exactly on Friday.
Exact aggregates when a test needs one
If a pipeline test needs a specific total, a specific rate, a specific count, declare it and the rows are solved to hit it, not approximated.
One command, or one import
Seed a database directly, or generate in Python and hand the dataframes to whatever your pipeline expects next.
pip install "misata[db]" misata seed postgresql://localhost/myapp_test --dry-run misata seed postgresql://localhost/myapp_test ✓ accounts : 200 rows ✓ users : 1,240 rows ✓ invoices : 4,800 rows 🔎 Verifying foreign keys against the database… ✓ users.account_id → accounts.id (0 orphans) ✓ invoices.account_id → accounts.id (0 orphans)
Questions
How do I generate test data without using production data?
Point Misata at your schema, either a live database connection or a schema definition, and it generates rows from that structure directly. Production is never read, so there is no real record to leak, mask, or explain in a security review.
Can synthetic data preserve foreign-key relationships?
Yes, and it's verified rather than assumed: parents are inserted before children, and Misata queries the database afterward to confirm every foreign key resolves and reports the orphan count, which should be zero.
Can I reproduce the same test dataset across runs?
Yes. Generation is deterministic from a seed, so the same schema, declarations, and seed produce the same dataset every time, which is what makes a flaky-looking test actually reproducible instead of dependent on whatever random data happened to generate that run.
How is this different from Faker or Mockaroo for test fixtures?
Faker and Mockaroo fill columns independently, with no model of how tables relate. Misata generates the whole schema together, so relationships, distributions, and any declared aggregate hold at once, which matters the moment a test involves a JOIN or a GROUP BY.
Further reading
How to generate whole relational datasets where every FK resolves and the totals reconcile, with a proof attached.
Sequential RNG reshuffles your whole fixture on a schema edit. Per-column streams keep the diff to what actually changed.
Two related pages, worth a look
If a security review is the actual blocker, the non-production data page addresses that directly. If you’re seeding a SaaS product’s own trial and demo accounts specifically, that’s its own page too.