Misata: synthetic data library

Sixty seconds

pip install "misata[duckdb]"

# misata.yamlseed: 7tables:  customers:    rows: 2000    columns:      customer_id: {type: int, unique: true, min: 1, max: 2000}      name: {type: text, text_type: name}      signed_up: {type: date, start: "2024-01-01", end: "2024-12-31"}  orders:    rows: 40000    columns:      order_id: {type: int, unique: true, min: 1, max: 1000000}      customer_id: {type: foreign_key}      channel: {type: categorical, choices: [web, app, store]}      amount: {type: float, min: 5, max: 900}      ordered_at: {type: date, start: "2025-01-01", end: "2025-12-31"}relationships:  - customers.customer_id -> orders.customer_idoutcome_curves:  - table: orders    column: amount    time_column: ordered_at    start_date: "2025-01-01"    curve_points:      - {month: 1, target_value: 180000}      - {month: 6, target_value: 240000}      - {month: 12, target_value: 310000}group_shares:  - table: orders    measure: amount    group_column: channel    shares: {web: 0.5, app: 0.3, store: 0.2}

misata generate -c misata.yaml -o data --attestpython data/verify.py

Attestation: data  result: PASSED  files pinned: 2  invariants held: 69 of 69
ok    2 file(s) match their attested sha256ok    69 of 69 invariant(s) hold

The 69 are derived from the YAML before a row exists: twelve monthly revenue totals (180,000.00 in January, 240,000.00 in June, 310,000.00 in December, straight lines between), the channel split inside every one of those months, the planned row count per month, zero orphaned customer_ids and unique keys. verify.py needs only the standard library and duckdb, and it shares no code with the generator.

Change one cent in one row and it says so:

FAIL  orders.csv: sha256 301165f9dc4e does not match the attested 670f89195826FAIL  orders.amount.month:1.outcome_curve_period: expected 180000.00, observed 180000.01FAIL  orders.amount.outcome_curve_total: expected 2945000.00, observed 2945000.01FAIL  orders.amount.web.2025-01.group_share_period: expected 90000.00, observed 90000.01FAIL  orders.amount.web.group_share_total: expected 1472500.00, observed 1472500.01FAIL  65 of 69 invariant(s) hold

Why declare instead of generate

Every test fixture has an oracle problem. To check a dbt model, a Spark job, a dashboard tile or a data agent you need input data and an answer you know independently of the code under test. Copying production gives you data without an answer. Hand-written fixtures give you an answer that rots. Random generators give you data whose answer you have to compute with the code you are testing.

Misata turns it around. The specification is the ground truth and the rows are derived from it. "January revenue is 180,000" is true before a single row exists, so your pipeline either returns 180,000.00 or it has a bug.

flowchart LR    A["Declare<br/>curves, shares, ledgers,<br/>keys, lifecycles"] --> B{"Can they<br/>all hold?"}    B -- "no" --> R["Refused, with the<br/>arithmetic and a fix"]    B -- "yes" --> C["Build rows<br/>closed form where it can,<br/>by construction elsewhere"]    C --> D["Write<br/>CSV, Parquet, JSONL,<br/>DuckDB, Postgres"]    D --> E["Attest<br/>DuckDB re-runs every<br/>derived invariant"]    E --> F["Certificate<br/>in-toto Statement<br/>plus verify.py"]

Three properties make this different from a generator with options:

• A declaration is honoured or refused, never quietly replaced. Declarations that cannot all hold stop generation with an error that names each one, shows the arithmetic and suggests the change.
• The certificate is checked by code that did not write the data. A bug in Misata's arithmetic shows up as a failed invariant, not a self-consistent wrong answer.
• The same spec, seed and version give the same bytes on Linux, macOS and Windows, so a fixture can live in git with its fingerprint.

Where this is going

Misata's aim is that nobody writes a Python script to make a dataset, of any kind. You say what has to be true; the engine works out the rows, refuses what cannot hold, and proves what it built.

It is not there yet, and it is getting there brick by brick. Each release adds things you can declare instead of programming, and a brick is laid only when it passes three tests: it can be declared in a few lines of YAML, it is refused before generation when it contradicts another declaration, and it is re-checked on the rows afterwards by code that did not write them.

One fact, two ways. What a script has to do to make a fact true, and what you write instead:

What still takes code, and where each one stands:

LIMITATIONS.md keeps the full list. When a line leaves this table, it is because the brick that replaces it has passed all three tests.

The evidence

What you can declare

Each declaration has a guarantee stated so you could test it, a list of the columns it owns, a cost and a refusal. LANGUAGE.md is the reference. All 25 declaration kinds are checked for feasibility before a row exists and re-checked on the rows afterwards; the ones marked ✓ are also in the DuckDB certificate.

Underneath any declaration, foreign keys resolve by construction (parents are built before children), text agrees with its row, money is skewed, timestamps have a daily rhythm and a few customers place most of the orders, so the data looks like a business before you declare anything. A sentence picks one of 18 story domains, from SaaS and fintech to healthcare, logistics and streaming, each with its tables, keys and calibrated distributions.

Pick your entry point

Pass a seed every time. The same seed is the same data.

Twelve recipes

Each is a folder with the schema, the commands, and the check that proves the declaration held. tools/stranger.py --clean runs every command on every page from a freshly built wheel in a clean virtualenv before each release, so the numbers on the pages are what you will see.

A status that implies its history

Declare the state machine once and every row's timestamps follow from its status. From the demo tenant:

stateDiagram-v2    direction LR    [*] --> trialing    trialing --> active    trialing --> churned    active --> past_due    active --> churned    past_due --> churned

lifecycles:  - name: subscription    table: subscriptions    state_column: status    start_column: started_at    initial: trialing    states:      - {name: trialing}      - {name: active, timestamp: activated_at}      - {name: past_due, timestamp: past_due_at}      - {name: churned, timestamp: churned_at, terminal: true}    transitions:      - [trialing, active]      - [trialing, churned]      - [active, past_due]      - [active, churned]      - [past_due, churned]    weights: {trialing: 0.07, active: 0.6, past_due: 0.07, churned: 0.26}

Of 1,200 subscriptions, exactly 720 are active, 312 churned, 84 past due and 84 still trialing: weights are counts, allocated by largest remainder, not probabilities. Every past-due row has an activation date and a past-due date, in that order and after it started; no active row has a churn date. The certificate checks both, row by row. A state reachable several ways splits its rows across every route: of the 312 churned, 156 left during the trial, 78 after being active and 78 after falling past due.

When it says no

$ misata explain bad.yamlGeneration would refuse: 1 conflict(s).1 declaration cannot be satisfied together:  1. [orders.channel] group_shares on amount      declared shares sum to 1.500 (web=0.6, app=0.6, store=0.3); a share of a whole cannot exceed 1.0      → Suggestion: scale the shares so they sum to 1.0, or drop a group. Misata will not renormalise them for you, because the result would be a specification you did not writeNothing was generated. Change the declarations each conflict names; its remedy says how.

misata explain prints the planned rows, what each declaration will write, how many invariants the certificate will carry and every conflict, without generating anything. Every command takes --json and prints one envelope; exit code 3 means the declarations cannot all hold and nothing was written.

With a coding agent

The agent designs the schema, Misata does the arithmetic, and DuckDB checks it. An agent cannot certify its own output; this is how it gets an answer it did not write.

[Install in Cursor]

claude mcp add misata -- uvx --from "misata[mcp]" misata-mcp   # Claude Codemisata mcp install --client all                                  # Cursor, Windsurf, Claude Desktop

Fifteen tools: generate_from_schema, generate_dataset, explain_schema, validate_yaml, inspect_schema, preview_story, list_domains, validate_domain, seed_database, create_sandbox, query_sandbox, solve_outcome, attest_dataset, audit_dataset and check_realism. AGENTS.md tells an agent which entry point fits which job and which claims the evidence supports.

What it is not

Misata imitates nothing unless you ask it to. It does not learn a joint distribution from your data. If you need a statistical twin of a specific private table, use a differentially private synthesizer with a stated epsilon. mimic fits to real rows and is not anonymous.

It is exact for what can be declared (sums, rates, shares, roll-ups, ledgers, keys, lifecycles) and not for arbitrary SQL. A model built on a window function or business logic Misata cannot invert still gets coherent, key-valid inputs, and you write the expected output. misata solve closes part of that gap by driving inputs through your real pipeline to a declared outcome within a stated tolerance, and the certificate says which tolerance.

Priors are English-first and US-leaning on money, and rates round to whole rows. LIMITATIONS.md has the full list and wins any argument with the rest of the docs.

Cite

If Misata helps your work, cite the software. GitHub's Cite this repository button reads the same metadata from CITATION.cff.

@software{rasin_misata,  title  = {Misata: declarative synthetic data},  author = {Rasin, Muhammed},  url    = {https://github.com/rasinmuhammed/misata}}

Contribute

git clone https://github.com/rasinmuhammed/misata && cd misatauv sync --all-extras && uv run pytest -qpython tools/stranger.py --clean    # every documented command, from the built wheel

A capability earns a place only if it can be declared and verified. The conformance suites in benchmarks/ are the contract: write the assertion first, watch it fail, then make it pass. MIT licensed.