Supported Connectors¶
Multi-schema discovery is available for JDBC-based connectors that support schema-level separation, and for the AWS Glue Native, Hive Native, and Unity Catalog Native connectors. DFS-based datastores (Amazon S3, Azure Datalake Storage, Google Cloud Storage) have no catalog/schema hierarchy, so they offer the same bulk creation with root paths instead: selecting several root paths creates one datastore per path.
Connector Reference¶
The table below shows each supported connector, the field used for catalog selection (if any), and the field used as the schema target for multi-select.
| Connector | Catalog Field | Schema Field |
|---|---|---|
| Athena | Catalog | Database |
| AWS Glue Native | None | Database |
| BigQuery | Project ID | Dataset ID |
| Databricks | Catalog | Database |
| DB2 | None | Schema |
| Dremio | None | Schema |
| Fabric Analytics | Database | Schema |
| Hive | None | Schema |
| Hive Native | None | Database |
| MariaDB | None | Database |
| Microsoft SQL Server | Database | Schema |
| MySQL | None | Database |
| Oracle | None | Schema |
| PostgreSQL | Database | Schema |
| Presto | Catalog | Schema |
| Redshift | Database | Schema |
| SAP HANA | Database | Schema |
| Snowflake | Database | Schema |
| Synapse | Database | Schema |
| Teradata | None | Database |
| Timescale DB | None | Schema |
| Unity Catalog Native | Catalog | Schema |
| Trino | Catalog | Schema |
Understanding the Columns¶
Catalog Field¶
The two columns give the label each field carries in the connector's own form, which differs between connectors: what plays the schema role is called Schema on some, Database on others, and Dataset ID on BigQuery.
The Catalog Field is the first-level hierarchy used to group schemas. Where it reads None, no catalog selection is needed: schemas are discovered directly from the connection.
Examples:
- PostgreSQL: The catalog is the
Database. You first select a database, then discover schemas within it. - BigQuery: The catalog is the
Project ID. You first select a project, then discover the datasets within it. - Oracle: There is no catalog level. Schemas are discovered directly.
Schema Field¶
The Schema Field is the target for multi-select. This is the level at which individual source datastores are created.
Note
On MySQL, MariaDB, and Teradata, the field carrying this role is labeled Database, because these systems have no separate schema concept. Each database is treated as a schema for multi-schema creation. Hive has no database field at all: its schema target is labeled Schema.
Two-Step vs. Single-Step Discovery¶
| Flow Type | Connectors | Description |
|---|---|---|
| Two-step (Catalog → Schema) | PostgreSQL, Snowflake, BigQuery, SQL Server, Synapse, Databricks, Redshift, Trino, Athena, SAP HANA, Fabric Analytics, Presto, Unity Catalog Native | First select a catalog, then discover and select schemas within it. |
| Single-step (Schema only) | Oracle, DB2, MySQL, MariaDB, Hive, Hive Native, AWS Glue Native, Dremio, Timescale DB, Teradata | Schemas are discovered directly without a catalog selection. |