Use-case presets
"Realistic" means different things to different jobs, so name the job:
misata.generate_from_schema(schema, preset="demo")misata generate --config misata.yaml --preset loador in the schema itself: preset: ml in YAML, "__preset__": "ml" in a dict.
| Preset | For | What it does |
|---|---|---|
demo | sales demos, dashboards, screenshots | shifts declared date ranges so the latest ends today; coherence repairs on; no deliberate mess |
test | unit and integration tests | caps every table at 200 rows; seed 0 if none is set; no mess |
load | load and performance tests | multiplies row counts (x10; apply_preset(..., scale=)); turns expensive coherence passes off; keeps popularity skew |
ml | ML pipelines and feature code | about 3% nulls, 1% outliers, 0.5% typos, 0.2% duplicates, with keys protected; correlations inferred from column names |
eval | agent and text-to-SQL evaluation | demo dates plus 2% nulls, typos and duplicates |
A preset only fills in what the schema did not say: a declared noise block,
row count or realism setting is kept. demo and eval leave dates alone when
curves or processes carry their own, because moving the columns without them
would break the declaration. misata.apply_preset(schema, name) returns the
adjusted copy if you want to inspect it before generating.
See A demo tenant that tells a story for
preset: demo in a complete recipe.
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