SQL DDL

SQL DDL synthetic data workflows

Use DDL ingestion when the source of truth is a table contract rather than a Python dictionary.

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

Getting started API reference GitHub

Parse a CREATE TABLE statement

The parser extracts table names, columns, types, and supported constraints for documented SQL dialects.

from great_generator import parse_ddl

contract = parse_ddl(ddl, dialect="databricks")
print(contract)

Generate from the contract

The parsed contract can be used as generation input so examples stay close to real database or lakehouse definitions.

from great_generator import generate_from_schema

df = generate_from_schema(contract, rows=1000)

Good fit

DDL ingestion is useful for contract testing, table onboarding, documentation examples, and lower-environment dataset creation.

Related documentation

Keep exploring

Generate synthetic data from schemasCreate synthetic test data from schema strings, dictionaries, Pandas DataFrames, and Spark schemas using semantic field detection. Query-aware synthetic dataGenerate synthetic data with required values, partition coverage, selectivity targets, and join coverage for SQL and data engineering tests. Spark, Databricks, Delta, and lakehouse workflowsUse Great Generator in Spark and Databricks notebooks to create synthetic datasets and write to Parquet, Delta, DBFS, ADLS, S3, GCS, or configured connectors. API reference overviewReview the main Great Generator public APIs for schema generation, SQL DDL parsing, relational generation, CDC, anomalies, advisors, and exports. GitHub repositorySource code, issues, releases, and project history. PyPI packageInstall Great Generator from Python Package Index.