> ## Documentation Index > Fetch the complete documentation index at: https://private-7c7dfe99-detect-table-modification.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Connecting to a data catalog > Connect ClickHouse to external data catalogs using the DataLakeCatalog database engine to expose catalog tables as native ClickHouse databases. export const galaxyOnClick = eventName => () => { try { if (typeof window !== "undefined" && window.galaxy && eventName) { window.galaxy.track(eventName, { interaction: "click" }); } } catch (e) {} }; export const BetaBadge = ({link, galaxyTrack, galaxyEvent}) => { if (link) { return Beta ; } return
Beta feature.  Learn more.
; }; In the [previous section](/guides/use-cases/data-warehousing/getting-started/querying-directly), you queried open table formats by passing storage paths directly. In practice, most organizations manage table metadata through a **data catalog** - a central registry that tracks table locations, schemas, and partitions. When you connect ClickHouse to a catalog using the [`DataLakeCatalog`](/reference/engines/database-engines/datalake) database engine, the entire catalog is exposed as a ClickHouse database. Every table in the catalog appears automatically and can be queried with full ClickHouse SQL - no need to know individual table paths or manage credentials per table. This guide walks through connecting to [Databricks Unity Catalog](https://www.databricks.com/product/unity-catalog). ClickHouse also supports the following catalogs - refer to each reference guide for full setup instructions: | Catalog | Reference guide | | -------------------- | --------------------------------------------------------------------------- | | AWS Glue | [AWS Glue catalog](/guides/use-cases/data-warehousing/glue-catalog) | | Iceberg REST Catalog | [REST catalog](/guides/use-cases/data-warehousing/rest-catalog) | | Lakekeeper | [Lakekeeper catalog](/guides/use-cases/data-warehousing/lakekeeper-catalog) | | Project Nessie | [Nessie catalog](/guides/use-cases/data-warehousing/nessie-catalog) | | Microsoft OneLake | [Fabric OneLake](/guides/use-cases/data-warehousing/onelake-catalog) |

Connecting to a Unity Catalog

For example purposes, we'll use the Unity catalog. [Databricks Unity Catalog](https://www.databricks.com/product/unity-catalog) provides centralized governance for Databricks lakehouse data. Databricks supports multiple data formats for their lakehouse. With ClickHouse, you can query Unity Catalog Delta and Iceberg tables that use external storage locations. This integration is currently only supported on AWS. Tables on Databricks managed storage are not supported because Unity Catalog does not provide credential vending for those locations. See the [Unity Catalog reference](/guides/use-cases/data-warehousing/unity-catalog) for details.

Configuring Unity in Databricks

To allow ClickHouse to interact with Unity catalog, you need to make sure your Unity Catalog is configured to allow interaction with an external reader. This can be achieved by following the[ "Enable external data access to Unity Catalog"](https://docs.databricks.com/aws/en/external-access/admin) guide. In addition to enabling external access, ensure the principal configuring the integration has the `EXTERNAL USE SCHEMA` [privilege](https://docs.databricks.com/aws/en/external-access/admin#external-schema) on the schema containing the tables. Once your catalog is configured, you must generate credentials for ClickHouse. Two different methods can be used, depending on your interaction mode with Unity: * For Iceberg clients, authenticate with a [service principal](https://docs.databricks.com/aws/en/dev-tools/auth/oauth-m2m). * For Delta clients, use a Personal Access Token ([PAT](https://docs.databricks.com/aws/en/dev-tools/auth/pat)).

Connect to the catalog

With the credentials, you can connect to the relevant endpoint to query the Iceberg or Delta tables. The [Unity catalog](/guides/use-cases/data-warehousing/unity-catalog) should be used for accessing data in the Delta format. ```sql theme={null} SET allow_experimental_database_unity_catalog = 1; CREATE DATABASE unity ENGINE = DataLakeCatalog('https://.cloud.databricks.com/api/2.1/unity-catalog') SETTINGS warehouse = 'CATALOG_NAME', catalog_credential = '', catalog_type = 'unity'; ``` ```sql theme={null} SET allow_database_iceberg = 1; CREATE DATABASE unity ENGINE = DataLakeCatalog('https://.cloud.databricks.com/api/2.1/unity-catalog/iceberg-rest') SETTINGS catalog_type = 'rest', catalog_credential = ':', warehouse = 'workspace', oauth_server_uri = 'https://.cloud.databricks.com/oidc/v1/token', auth_scope = 'all-apis,sql'; ```

List tables

Once the connection has been established to your catalog, you can list the tables. ```sql theme={null} SHOW TABLES FROM unity ``` ```response theme={null} ┌─name───────────────────────────────────────────────┐ │ unity.logs │ │ unity.single_day_log │ └────────────────────────────────────────────────────┘ 31 rows in set. ```

Exploring table schemas

We can use the standard `SHOW CREATE TABLE` command to see how the tables were created. **Backticks required** Note the need to specify the namespace and the table name, surrounded with backticks - ClickHouse doesn't support more than one namespace. The following assumes querying the REST iceberg catalog: ```sql theme={null} SHOW CREATE TABLE unity.`icebench.single_day_log` CREATE TABLE unity.`icebench.single_day_log` ( `pull_request_number` Nullable(Int64), `commit_sha` Nullable(String), `check_start_time` Nullable(DateTime64(6, 'UTC')), `check_name` Nullable(String), `instance_type` Nullable(String), `instance_id` Nullable(String), `event_date` Nullable(Date32), `event_time` Nullable(DateTime64(6, 'UTC')), `event_time_microseconds` Nullable(DateTime64(6, 'UTC')), `thread_name` Nullable(String), `thread_id` Nullable(Decimal(20, 0)), `level` Nullable(String), `query_id` Nullable(String), `logger_name` Nullable(String), `message` Nullable(String), `revision` Nullable(Int64), `source_file` Nullable(String), `source_line` Nullable(Decimal(20, 0)), `message_format_string` Nullable(String) ) ENGINE = Iceberg('s3://...') ```

Querying a table

All ClickHouse functions are supported. Again, the namespace and table name should be delimited with backticks. ```sql theme={null} SELECT count() FROM unity.`icebench.single_day_log` ``` ```response theme={null} ┌───count()─┐ │ 282634391 │ -- 282.63 million └───────────┘ 1 row in set. Elapsed: 1.265 sec. ``` For full setup instructions, see the [Unity catalog reference guide](/guides/use-cases/data-warehousing/unity-catalog).