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Misata

Declare what must be true. Get data that makes it true. Prove it.

Misata is a declarative synthetic data generator for Python. You write down the facts a dataset has to satisfy, from a monthly revenue curve to a balanced ledger to a foreign key that must never dangle, and the engine builds multi-table data that satisfies all of them at once. Then DuckDB checks the files on disk and writes a certificate anyone can re-run.

Docs · Use cases · The language · Limitations · Studio · Changelog

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<!-- mcp-name: io.github.rasinmuhammed/misata -->

Sixty seconds

pip install "misata[duckdb]"

# misata.yaml
seed: 7
tables:
  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_id
outcome_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 --attest
python data/verify.py

Attestation: data
  result: PASSED
  files pinned: 2
  invariants held: 69 of 69

ok    2 file(s) match their attested sha256
ok    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.

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/curve-dark.svg"> <img src="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/curve-light.svg" width="680" alt="The quickstart generated and summed by month. Twelve stacked bars split into web, app and store rise from 180,000.00 in January through 240,000.00 in June to 310,000.00 in December. Each bar ends exactly at the declared monthly total, and the channels hold 50, 30 and 20 percent. The largest gap between declared and generated over 48 totals and cells is 0.00."> </picture>

_{The YAML above, generated and summed by month and channel. Each bar is a month of generated revenue split by channel; the cap is the total the YAML declares. Drawn by <a href="tools/readme_charts.py">readme_charts.py</a> from a fresh run of this README's own YAML.} </div>

Change one cent in one row and it says so:

FAIL  orders.csv: sha256 301165f9dc4e does not match the attested 670f89195826
FAIL  orders.amount.month:1.outcome_curve_period: expected 180000.00, observed 180000.01
FAIL  orders.amount.outcome_curve_total: expected 2945000.00, observed 2945000.01
FAIL  orders.amount.web.2025-01.group_share_period: expected 90000.00, observed 90000.01
FAIL  orders.amount.web.group_share_total: expected 1472500.00, observed 1472500.01
FAIL  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.

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/bricks-dark.svg"> <img src="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/bricks-light.svg" width="680" alt="A wall of bricks, one per guarantee Misata understands today, in three courses: keys, rules and relationships such as foreign keys, unique keys and CHECK rules; exact counts and sums such as outcome_curves, rate_curves and group_shares; and state, structure, time and texture such as lifecycles, dag_edges and time_grids. Bricks the DuckDB certificate re-checks are filled. Above them sits an unfinished course of dashed bricks for what still has to be scripted by hand."> </picture>

_{Every brick is something you declare instead of code. Drawn from <a href="misata/registry.py">misata/registry.py</a>, the list a contract test holds the code to, so the wall cannot claim a brick the engine does not have.} </div>

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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

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/gauntlet-dark.svg"> <img src="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/gauntlet-light.svg" width="680" alt="The Gauntlet, assertions held per section. Misata holds 136 of 136. A Faker script with the same columns holds 78 of 136: it ties on keys, value domains, diamond joins, arithmetic and distributions, and loses on time ordering, status-implied fields, cross-table totals, geography, lifecycles, missingness, late arrivals and grids."> </picture>

_{The Gauntlet: 13 tables and 136 SQL assertions run by DuckDB. The Faker script ties wherever a fact is about one column at a time and loses wherever columns, rows or tables have to agree. <a href="benchmarks/gauntlet.py">Source</a>, rerun with <code>python -m benchmarks.gauntlet</code>.} </div>

<br/>

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/detection-dark.svg"> <img src="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/detection-light.svg" width="680" alt="Detection AUC, lower is better, 0.5 means a classifier cannot tell generated rows from real ones. Olist orders: Misata from a declared schema 0.63, Misata from a sentence 0.73, Faker script 0.78, SDV fitted to the real data 0.71, Misata mimic 0.54. NYC taxi trips: 0.75, 0.80, 0.79, 0.68 and 0.54."> </picture>

_{A gradient-boosted classifier tries to tell generated rows from real ones; mean of five seeds. Without seeing a real row, a declared schema beats SDV trained on the real orders. On taxi trips SDV wins: blind fares are a miss. <a href="benchmarks/realism_bench.py">Source</a>, numbers in <a href="benchmarks/results/summary.json">summary.json</a>.} </div>

<br/>

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.

<div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/history-dark.svg"> <img src="https://raw.githubusercontent.com/rasinmuhammed/misata/main/docs/assets/readme/history-light.svg" width="680" alt="Every demo-tenant subscription grouped by the timestamps it carries. 84 trialing rows have only started_at. 720 active rows add activated_at. 84 past-due rows add past_due_at. 156 churned straight from trial with started_at and churned_at, 78 churned after being active, and 78 churned after falling past due, each carrying every timestamp on its route. Rows matching no declared route: 0."> </picture>

_{The demo tenant's 1,200 subscriptions, grouped by which timestamps are set. Six routes, and no row outside them. Drawn by <a href="tools/readme_charts.py">readme_charts.py</a> from the recipe's own YAML.} </div>

When it says no

$ misata explain bad.yaml
Generation 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 write
Nothing 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.

<!-- stranger: skip, installs into the reader's agent clients -->

claude mcp add misata -- uvx --from "misata[mcp]" misata-mcp   # Claude Code
misata 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

<!-- stranger: skip, clones the repository -->

git clone https://github.com/rasinmuhammed/misata && cd misata
uv sync --all-extras && uv run pytest -q
python 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.