FAQ

FAQ

Is this a production-data anonymization tool?

No. Great Generator creates synthetic data and does not transform production records.

What should industry users start with?

Start with generate_from_schema when you already know the expected table structure.

Which schema inputs work today?

Plain mappings, Pandas dtype mappings and DataFrames, compact DDL, PySpark StructType and DataFrames, TableSchema, and DomainSchema. See the support matrix.

Does full SQL CREATE TABLE work?

Not yet. Use compact column DDL or a Python mapping.

Are JSON Schema and YAML schema files supported?

Not as generate_from_schema inputs. They are planned. JSON, TOML, and simple YAML dataset recipes are a separate supported feature.

Can it return Spark DataFrames?

Yes, when Spark context is available or engine="spark" is selected. Single-table arbitrary-schema generation currently creates values locally before creating the Spark DataFrame.

Can I write to CSV, Parquet, Delta, databases, S3, ADLS, or GCS?

Yes, through normal Pandas or Spark writers and separately installed connectors. Great Generator does not configure credentials or platform permissions.

Is a seed mandatory?

No. Use a seed only when repeatable output matters for a test, benchmark, or experiment.

Can it generate millions of rows?

Row-count capability depends on engine, schema, memory, compute, and storage. Use chunking or Spark-native domain generation for very large workloads and benchmark in your own environment.

When should I use domain packs?

Use generate_domain for ready-made demos, learning, tutorials, and examples where you do not already have a schema.