--- name: watsonxdata-data_stream_databricks3presto title: Querying Databricks Iceberg tables using Presto engine description: '' last-updated: 2026-04-08 --- > ## Documentation Index > The table of contents for this documentation set is at https://cloud.ibm.com/docs/watsonxdata?format=markdown > The index for all IBM Cloud docs is at: https://cloud.ibm.com/docs/llms.txt > Use these files to discover more information as needed. {:javascript: #javascript .ph data-hd-programlang='javascript'} {:java: #java .ph data-hd-programlang='java'} {:ruby: #ruby .ph data-hd-programlang='ruby'} {:php: #php .ph data-hd-programlang='php'} {:python: #python .ph data-hd-programlang='python'} {:external: target="_blank" .external} {:shortdesc: .shortdesc} {:codeblock: .codeblock} {:screen: .screen} {:tip: .tip} {:important: .important} {:note: .note} {:deprecated: .deprecated} {:pre: .pre} {:video: .video} # Querying Databricks Iceberg tables using Presto engine {: #data_stream_databricks3presto} ## About this task {: #data_stream_databricks3presto1} You can query remote Databricks Iceberg tables using the IBM® watsonx.data Presto engine by registering Databricks as a custom data source for zero-copy data federation. - Presto connects to Databricks through the Iceberg REST Catalog API interface, not Unity Catalog API directly. - Presto does not currently support vended-credentials for Databricks integration. - You must configure explicit storage credentials (AWS S3 and Azure Data Lake Storage Gen2) to access the underlying data files. - Presto supports Iceberg tables only; Delta Lake tables are not supported. - For general information about Databricks Unity Catalog integration, see [Integrating Databricks Unity Catalog in watsonx.data](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-data_stream_databricks1&format=markdown). ## Before you begin {: #data_stream_databricks3presto2} Complete the prerequisites outlined in [Integrating Databricks Unity Catalog in watsonx.data](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-data_stream_databricks1&format=markdown), including: - Databricks workspace with Unity Catalog enabled - Iceberg tables created in Databricks Unity Catalog - Personal Access Token with `unity-catalog` API scope - **Workspace URL:** `https://.cloud.databricks.com` - Unity Catalog permissions configured - Provisioned Presto engine in watsonx.data - **Iceberg REST Catalog endpoint:** `https://.cloud.databricks.com/api/2.1/unity-catalog/iceberg-rest` - **Catalog name:** Name of the Unity Catalog containing Iceberg tables - Access credentials for the external storage location (AWS S3 or Azure) Use of vended credentials is currently not supported. Hence, Presto requires explicit storage credentials. {: note} ## Procedure {: #data_stream_databricks3presto3} 1. Register Databricks as a custom data source 1. In the watsonx.data console, click **Infrastructure manager**. 2. Click **Add component > Add data source**. 3. Select **Custom** as the data source type. 4. Enter a display name (e.g., `databricks_iceberg`). 5. In the **Properties** section, add the following properties: **For AWS S3:** ```properties connector.name=iceberg iceberg.catalog.type=rest iceberg.rest.uri=https://.cloud.databricks.com/api/2.1/unity-catalog/iceberg-rest iceberg.rest.auth.type=OAUTH2 iceberg.rest.auth.oauth2.token= iceberg.catalog.warehouse= hive.s3.aws-access-key= hive.s3.aws-secret-key= ``` {: codeblock} **For Azure Data Lake Storage Gen2:** ```properties connector.name=iceberg iceberg.catalog.type=rest iceberg.rest.uri=https:///api/2.1/unity-catalog/iceberg-rest iceberg.rest.auth.type=OAUTH2 iceberg.rest.auth.oauth2.token= iceberg.catalog.warehouse= hive.azure.abfs-storage-account= hive.azure.abfs-access-key= ``` {: codeblock} Replace the placeholders in the 2 storage properties as follows: - ``: Your Databricks workspace URL - ``: Your Databricks personal access token - ``: The Unity Catalog name containing your Iceberg tables - Storage credential placeholders with your actual credentials 6. Click **Create**. 2. Create a catalog for the data source 1. Click **Data manager > Catalogs**. 2. Click **Create catalog**. 3. Select **Iceberg** as the catalog type. 4. Enter a catalog name (e.g., `databricks_catalog`). 5. In the **Data source** field, select the custom data source you created (`databricks_iceberg`). 6. Click **Create**. 3. Associate the catalog with Presto engine 1. Click **Infrastructure manager**. 2. Select your Presto engine from the list. 3. Click **Associate catalog**. 4. Select the catalog you created (`databricks_catalog`). 5. Click **Associate**. 4. Query Databricks Iceberg tables 1. Click **Query workspace**. 2. Select your Presto engine from the engine dropdown. 3. Run queries against your remote Databricks Iceberg tables using fully qualified table names: ```sql -- List available schemas (namespaces) SHOW SCHEMAS IN databricks_catalog; -- List tables in a schema SHOW TABLES IN databricks_catalog.; -- Describe table structure DESCRIBE databricks_catalog..; -- Query data SELECT * FROM databricks_catalog.. LIMIT 10; -- Get row count SELECT COUNT(*) FROM databricks_catalog..; -- Filter and aggregate SELECT column1, COUNT(*) as count, AVG(column2) as avg_value FROM databricks_catalog.. WHERE column3 > 100 GROUP BY column1 ORDER BY count DESC; ``` {: codeblock} ## Results {: #data_stream_databricks3presto4} You can now query Iceberg tables from Databricks Unity Catalog using Presto. The queries execute directly on the data in the external storage location without copying data into watsonx.data. ## Example queries and outputs {: #data_stream_databricks3presto5} List schemas in a catalog: ```sql SHOW SCHEMAS IN databricks_catalog; ``` {: codeblock} Output: ```sql Schema -------------------------- default delta_share_demo feb14schema information_schema mrmadira_external_schema tpcdsdbiceberg_10tb_partitioned_uc ``` {: screen} List tables in a schema: ```sql SHOW TABLES IN databricks_catalog.feb14schema; ``` {: codeblock} Output: ```sql Table ----------------- avengers cims_test_result iceberg_orders mrmadira_csv_table ``` {: screen} Describe table structure: ```sql DESCRIBE databricks_catalog.feb14schema.iceberg_orders; ``` {: codeblock} Output: ```sql Column | Type | Extra | Comment -------------|-----------------------|-------|-------- order_id | bigint | | customer_id | bigint | | order_ts | timestamp(6) | | total_amt | decimal(12,2) | | ``` {: screen} Query a table: ```sql SELECT * FROM databricks_catalog.feb14schema.iceberg_orders LIMIT 10; ``` {: codeblock} Output: ```sql order_id | customer_id | order_ts | total_amt ---------|-------------|---------------------|---------- 22222 | 22222 | 2022-01-01 00:00:00 | 100.00 ``` {: screen} ## Related information {: #data_stream_databricks3presto6} - [Integrating Databricks Unity Catalog in watsonx.data](https://cloud.ibm.com/docs/watsonxdata?topic=watsonxdata-data_stream_databricks1&format=markdown) - [Databricks Unity Catalog documentation](https://docs.databricks.com/data-governance/unity-catalog/index.html) - [Unity Catalog privileges and securable objects](https://docs.databricks.com/data-governance/unity-catalog/manage-privileges/privileges.html)