Use a connector as a data source

If you use the web application, select a source connector to add data to a new generator.

Steps

1. From the Generators page, open an untrained generator or create a new one by clicking + New generator.
2. Click Add data.
3. Click Connect to source.
4. Select a source connector from the list from which to add data.
5. Select a database schema and a table.
6. (Optional) Select Include child tables to add any tables that reference the currently selected table with a foreign key.

Result

The selected tables (and, if selected, any referencing child tables) are added to the generator.

If you use the Python client, add the name, location, and sourceConnectorId to the table dictionary in the tables list.

• name key - define the table name as it appears in the generator and in the synthetic datasets that you create with the generator
• location key
  • for databases with a schema, define the location as SCHEMA.TABLE
  • for databases without a schema, define the location as TABLE
  • for cloud buckets, define the location as the path to the table file/folder/subfolder/file.csv
• sourceConnectorId - ID of the source connector

Here is an Python example for a source database with a schema. Replace DB_WITH_SCHEMA_CONNECTOR_ID with the connector ID, SCHEMA with the schema name, and TABLE with the table name in the database.

config = {
    'name': 'US census',
    'tables': [
        {
            'name': 'census',
            'sourceConnectorId': 'DB_WITH_SCHEMA_CONNECTOR_ID',
            "location": "SCHEMA.TABLE",
        }
    ]
}
g = mostly.train(config=config)

For a source database with no schema. Replace DB_NO_SCHEMA_CONNECTOR_ID with the connector ID and TABLE with the table name in the database.

config = {
    'name': 'US census',
    'tables': [
        {
            'name': 'census',
            'sourceConnectorId': 'DB_NO_SCHEMA_CONNECTOR_ID',
            "location": "TABLE",
        }
    ]
}
g = mostly.train(config=config)

For a cloud bucket with a path to the table file.

config = {
    'name': 'US census',
    'tables': [
        {
            'name': 'census',
            'sourceConnectorId': 'CLOUD_BUCKET_CONNECTOR_ID',
            "location": "/FOLDER/FILE.CSV",
        }
    ]
}
g = mostly.train(config=config)