dbt integration

Generate synthetic data from dbt model metadata

Create lower-environment model data from common dbt schema.yml and target/manifest.json metadata.

Great Generator creates synthetic data. It does not anonymize, mask, de-identify, or transform production records.

Getting started Schema inputs API reference

schema.yml

from great_generator import generate_from_dbt_schema

df = generate_from_dbt_schema(
    "models/schema.yml",
    model_name="customers",
    rows=1000,
)

manifest.json

from great_generator import generate_from_dbt_manifest

df = generate_from_dbt_manifest(
    "target/manifest.json",
    model_name="customers",
    rows=1000,
)

Mapped metadata

Column names, data types, descriptions, not_null tests, unique tests, accepted_values tests, and relationship hints where possible.

Keep exploring

Schema generationMappings, DDL strings, DataFrames, Spark schemas, and schema-source inputs. SQL DDLCanonical contracts and documented CREATE TABLE parsing. Query-aware generationRequired values, partitions, selectivity, joins, and coverage reports. API referenceMain public APIs and usage patterns.