# Create Azure Blob Storage BigLake tables Azure Blob Storage BigLake tables let you query data in Blob Storage from BigQuery by using access delegation. This approach decouples access to the [BigLake table](https://docs.cloud.google.com/bigquery/docs/biglake-intro) from access to the underlying datastore, so users need permissions only on the table. This document describes how to create an Azure Blob Storage BigLake table. For information about how data flows between BigQuery and Blob Storage, see [Data flow when querying data](https://docs.cloud.google.com/bigquery/docs/omni-introduction#query-data). ## Before you begin Ensure that you have a [connection to access data in your Blob Storage](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-connection). ### Required roles To get the permissions that you need to create an external table, ask your administrator to grant you the [BigQuery Admin](https://docs.cloud.google.com/iam/docs/roles-permissions/bigquery#bigquery.admin) (`roles/bigquery.admin`) IAM role on your dataset. For more information about granting roles, see [Manage access to projects, folders, and organizations](https://docs.cloud.google.com/iam/docs/granting-changing-revoking-access). This predefined role contains the permissions required to create an external table. To see the exact permissions that are required, expand the **Required permissions** section: #### Required permissions The following permissions are required to create an external table: - `bigquery.tables.create` - `bigquery.connections.delegate` You might also be able to get these permissions with [custom roles](https://docs.cloud.google.com/iam/docs/creating-custom-roles) or other [predefined roles](https://docs.cloud.google.com/iam/docs/roles-overview#predefined). ## Create a dataset Before you create an external table, you need to create a dataset in the [supported region](https://docs.cloud.google.com/bigquery/docs/omni-introduction#locations). Select one of the following options:
### Console 1. Go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the left pane, click **Explorer**. 3. In the **Explorer** pane, select the project where you want to create the dataset. 4. Click **View actions** , and then click **Create dataset**. 5. On the **Create dataset** page, specify the following details: 1. For **Dataset ID** enter a unique dataset [name](https://docs.cloud.google.com/bigquery/docs/datasets#dataset-naming). 2. For **Data location** choose a [supported region](https://docs.cloud.google.com/bigquery/docs/omni-introduction#locations). 3. Optional: To delete tables automatically, select the **Enable table expiration** checkbox and set the **Default maximum table age** in days. Data in Azure is not deleted when the table expires. 4. If you want to use [default collation](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/collation-concepts), expand the **Advanced options** section and then select the **Enable default collation** option. 5. Click **Create dataset**. ### SQL Use the [`CREATE SCHEMA` DDL statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_schema_statement). The following example create a dataset in the `azure-eastus2` region: 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the query editor, enter the following statement: ```googlesql CREATE SCHEMA mydataset OPTIONS ( location = 'azure-eastus2'); ```
3. Click **Run**.
For more information about how to run queries, see [Run an interactive query](https://docs.cloud.google.com/bigquery/docs/running-queries#queries). ### bq In a command-line environment, create a dataset using the [`bq mk` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#mk-dataset): ```bash bq --location=LOCATION mk \ --dataset \ PROJECT_ID:DATASET_NAME ``` The `--project_id` parameter overrides the default project. Replace the following: - `LOCATION`: the location of your dataset For information about supported regions, see [Locations](https://docs.cloud.google.com/bigquery/docs/omni-introduction#locations). After you create a dataset, you can't change its location. You can set a default value for the location by using the [`.bigqueryrc` file](https://docs.cloud.google.com/bigquery/docs/bq-command-line-tool#setting_default_values_for_command-line_flags). - `PROJECT_ID`: your project ID - `DATASET_NAME`: the name of the dataset that you want to create To create a dataset in a project other than your default project, add the project ID to the dataset name in the following format: `PROJECT_ID:DATASET_NAME`. ## Create BigLake tables on unpartitioned data Select one of the following options: ### Console 1. Go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the left pane, click **Explorer**: ![Highlighted button for the Explorer pane.](https://docs.cloud.google.com/static/bigquery/images/explorer-tab.png) If you don't see the left pane, click **Expand left pane** to open the pane. 3. In the **Explorer** pane, expand your project, click **Datasets**, and then select a dataset. 4. In the **Dataset info** section, click **Create table**. 5. On the **Create table** page, in the **Source** section, do the following: 1. For **Create table from** , select **Azure Blob Storage**. 2. For **Select Azure Blob Storage path** , enter a Blob Storage path using the following format: `azure://AZURE_STORAGE_ACCOUNT_NAME.blob.core.windows.net/CONTAINER_NAME/FILE_PATH` Replace the following: - `AZURE_STORAGE_ACCOUNT_NAME`: The name of the Blob Storage account. The account's region should be the same as the dataset's region. - `CONTAINER_NAME`: The name of the Blob Storage container. - `FILE_PATH`: The data path that points to the Blob Storage data. For example, for a single CSV file, `FILE_PATH` can be `myfile.csv`. 3. For **File format** , select the data format in Azure. Supported formats are **AVRO** , **CSV** , **DELTA_LAKE** , **ICEBERG** , **JSONL** , **ORC** , and **PARQUET**. 6. In the **Destination** section, do the following: 1. For **Dataset**, choose the appropriate dataset. 2. In the **Table** field, enter the name of the table. 3. Verify that **Table type** is set to **External table**. 4. For **Connection ID** , choose the appropriate connection ID from the drop-down. For information about connections, see [Connect to Blob Storage](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-connection). 7. In the **Schema** section, you can either enable [schema auto-detection](https://docs.cloud.google.com/bigquery/docs/schema-detect) or manually specify a schema if you have a source file. If you don't have a source file, you must manually specify a schema. - To enable schema auto-detection, select the **Auto-detect** option. - To manually specify a schema, leave the **Auto-detect** option unchecked. Enable **Edit as text** and enter the table schema as a [JSON array](https://docs.cloud.google.com/bigquery/docs/schemas#specifying_a_json_schema_file). 8. Click **Create table**. > [!NOTE] > **Note:** For Avro, Parquet, and ORC file formats, you do not need to specify the table schema because BigQuery Omni autodetects the table schema from the source file. For CSV, JSON, and Google Sheets file formats, for schema autodetection, check **Auto detect**. ### SQL To create a BigLake table, use the [`CREATE EXTERNAL TABLE`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_external_table_statement) statement with the `WITH CONNECTION` clause: 1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the query editor, enter the following statement: ```googlesql CREATE EXTERNAL TABLE DATASET_NAME.TABLE_NAME WITH CONNECTION `AZURE_LOCATION.CONNECTION_NAME` OPTIONS ( format = 'DATA_FORMAT', uris = ['azure://AZURE_STORAGE_ACCOUNT_NAME.blob.core.windows.net/CONTAINER_NAME/FILE_PATH']); ``` Replace the following: - `DATASET_NAME`: the name of the dataset you created - `TABLE_NAME`: the name you want to give to this table - `AZURE_LOCATION`: an Azure location in Google Cloud, such as `azure-eastus2` - `CONNECTION_NAME`: the name of the connection you created - `DATA_FORMAT`: any of the supported [BigQuery federated formats](https://docs.cloud.google.com/bigquery/external-data-sources), such as `AVRO`, `CSV`, `DELTA_LAKE`, or `ICEBERG` ([preview](https://cloud.google.com/products/#product-launch-stages)) - `AZURE_STORAGE_ACCOUNT_NAME`: the name of the Blob Storage account - `CONTAINER_NAME`: the name of the Blob Storage container - `FILE_PATH`: the data path that points to the Blob Storage data
3. Click **Run**.
For more information about how to run queries, see [Run an interactive query](https://docs.cloud.google.com/bigquery/docs/running-queries#queries). Example: ```googlesql CREATE EXTERNAL TABLE absdataset.abstable WITH CONNECTION `azure-eastus2.abs-read-conn` OPTIONS ( format = 'CSV', uris = ['azure://account_name.blob.core.windows.net/container/path/file.csv']); ``` ### bq Create a [table definition file](https://docs.cloud.google.com/bigquery/external-table-definition): ```bash bq mkdef \ --source_format=DATA_FORMAT \ --connection_id=AZURE_LOCATION.CONNECTION_NAME \ "azure://AZURE_STORAGE_ACCOUNT_NAME.blob.core.windows.net/CONTAINER_NAME/FILE_PATH" > table_def ``` Replace the following: - `DATA_FORMAT`: any of the supported [BigQuery federated formats](https://docs.cloud.google.com/bigquery/external-data-sources), such as `AVRO`, `CSV`, `ICEBERG`, or `PARQUET` - `AZURE_LOCATION`: an Azure location in Google Cloud, such as `azure-eastus2` - `CONNECTION_NAME`: the name of the connection that you created - `AZURE_STORAGE_ACCOUNT_NAME`: the name of the Blob Storage account - `CONTAINER_NAME`: the name of the Blob Storage container - `FILE_PATH`: the data path that points to the Blob Storage data Next, create the BigLake table: ```bash bq mk --external_table_definition=table_def DATASET_NAME.TABLE_NAME ``` Replace the following: - `DATASET_NAME`: the name of the dataset that you created - `TABLE_NAME`: the name that you want to give to this table For example, the following commands create a new BigLake table, `my_dataset.my_table`, which can query your Blob Storage data that's stored at the path `azure://account_name.blob.core.windows.net/container/path` and has a read connection in the location `azure-eastus2`: ```bash bq mkdef \ --source_format=AVRO \ --connection_id=azure-eastus2.read-conn \ "azure://account_name.blob.core.windows.net/container/path" > table_def bq mk \ --external_table_definition=table_def my_dataset.my_table ``` > [!NOTE] > **Note:** To override the default project, use the `--project_id=PROJECT_ID` parameter. Replace `PROJECT_ID` with the ID of your Google Cloud project. ### API Call the [`tables.insert` method](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables/insert) API method, and create an [`ExternalDataConfiguration`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables#externaldataconfiguration) in the [`Table` resource](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables#Table) that you pass in. Specify the `schema` property or set the `autodetect` property to `true` to enable schema auto detection for supported data sources. Specify the `connectionId` property to identify the connection to use for connecting to Blob Storage. ## Create BigLake tables on partitioned data You can create a BigLake table for Hive partitioned data in Blob Storage. After you create an externally partitioned table, you can't change the partition key. You need to recreate the table to change the partition key. To create a BigLake table based on Hive partitioned data, select one of the following options: ### Console 1. Go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the left pane, click **Explorer**: ![Highlighted button for the Explorer pane.](https://docs.cloud.google.com/static/bigquery/images/explorer-tab.png) If you don't see the left pane, click **Expand left pane** to open the pane. 3. In the **Explorer** pane, expand your project, click **Datasets**, and then select a dataset. 4. Click **Create table** . The **Create table** pane opens. 5. In the **Source** section, specify the following details: 1. For **Create table from**, select one of the following options: - **Amazon S3** - **Azure Blob Storage** 2. Provide the path to the folder, using [wildcards](https://docs.cloud.google.com/bigquery/docs/batch-loading-data#load-wildcards). For example: - For Amazon S3: `s3://mybucket/*` - For Blob Storage: `azure://mystorageaccount.blob.core.windows.net/mycontainer/*` The folder must be in the same location as the dataset that contains the table you want to create, append, or overwrite. 3. From the **File format** list, select the file type. 4. Select the **Source data partitioning** checkbox, and then specify the following details: 1. For **Select Source URI Prefix** , enter the URI prefix. For example, `s3://mybucket/my_files`. 2. Optional: To require a partition filter on all queries for this table, select the **Require partition filter** checkbox. Requiring a partition filter can reduce cost and improve performance. For more information, see [Requiring predicate filters on partition keys in queries](https://docs.cloud.google.com/bigquery/docs/hive-partitioned-queries-gcs#requiring_predicate_filters_on_partition_keys_in_queries). 3. In the **Partition inference mode** section, select one of the following options: - **Automatically infer types** : set the partition schema detection mode to `AUTO`. - **All columns are strings** : set the partition schema detection mode to `STRINGS`. - **Provide my own** : set the partition schema detection mode to `CUSTOM` and manually enter the schema information for the partition keys. For more information, see [Custom partition key schema](https://docs.cloud.google.com/bigquery/docs/hive-partitioned-loads-gcs#custom_partition_key_schema). 6. In the **Destination** section, specify the following details: 1. For **Project**, select the project in which you want to create the table. 2. For **Dataset**, select the dataset in which you want to create the table. 3. For **Table**, enter the name of the table that you want to create. 4. For **Table type** , verify that **External table** is selected. 5. For **Connection ID**, select the connection that you created earlier. 7. In the **Schema** section, you can either enable [schema auto-detection](https://docs.cloud.google.com/bigquery/docs/schema-detect) or manually specify a schema if you have a source file. If you don't have a source file, you must manually specify a schema. - To enable schema auto-detection, select the **Auto-detect** option. - To manually specify a schema, leave the **Auto-detect** option unchecked. Enable **Edit as text** and enter the table schema as a [JSON array](https://docs.cloud.google.com/bigquery/docs/schemas#specifying_a_json_schema_file). 8. To ignore rows with extra column values that don't match the schema, expand the **Advanced options** section and select **Unknown values**. 9. Click **Create table**. ### SQL Use the [`CREATE EXTERNAL TABLE` DDL statement](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_external_table_statement):

1. In the Google Cloud console, go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. In the query editor, enter the following statement: ```googlesql CREATE EXTERNAL TABLE `PROJECT_ID.DATASET.EXTERNAL_TABLE_NAME` WITH PARTITION COLUMNS ( PARTITION_COLUMN PARTITION_COLUMN_TYPE, ) WITH CONNECTION `PROJECT_ID.REGION.CONNECTION_ID` OPTIONS ( hive_partition_uri_prefix = "HIVE_PARTITION_URI_PREFIX", uris=['FILE_PATH'], format ="TABLE_FORMAT" ); ``` Replace the following: - `PROJECT_ID`: the name of your project in which you want to create the table---for example, `myproject` - `DATASET`: the name of the BigQuery dataset that you want to create the table in---for example, `mydataset` - `EXTERNAL_TABLE_NAME`: the name of the table that you want to create---for example, `mytable` - `PARTITION_COLUMN`: the name of the partitioning column - `PARTITION_COLUMN_TYPE`: the type of the partitioning column - `REGION`: the region that contains the connection---for example, `us` - `CONNECTION_ID`: the name of the connection---for example, `myconnection` - `HIVE_PARTITION_URI_PREFIX`: hive partitioning URI prefix---for example:

- `s3://mybucket/` - `azure://mystorageaccount.blob.core.windows.net/mycontainer/` - `FILE_PATH`: path to the data source for the external table that you want to create---for example:

- `s3://mybucket/*.parquet` - `azure://mystorageaccount.blob.core.windows.net/mycontainer/*.parquet` - `TABLE_FORMAT`: the format of the table that you want to create---for example, `PARQUET`
3. Click **Run**.
For more information about how to run queries, see [Run an interactive query](https://docs.cloud.google.com/bigquery/docs/running-queries#queries). **Examples** The following example creates a BigLake table over partitioned data in Amazon S3. The schema is auto-detected. ```googlesql CREATE EXTERNAL TABLE `my_dataset.my_table` WITH PARTITION COLUMNS ( sku STRING, ) WITH CONNECTION `us.my-connection` OPTIONS( hive_partition_uri_prefix = "s3://mybucket/products", uris = ['s3://mybucket/products/*'] ); ``` The following example creates a BigLake table over partitioned data in Blob Storage. The schema is specified. ```googlesql CREATE EXTERNAL TABLE `my_dataset.my_table` ( ProductId INTEGER, ProductName, STRING, ProductType, STRING ) WITH PARTITION COLUMNS ( sku STRING, ) WITH CONNECTION `us.my-connection` OPTIONS( hive_partition_uri_prefix = "azure://mystorageaccount.blob.core.windows.net/mycontainer/products", uris = ['azure://mystorageaccount.blob.core.windows.net/mycontainer/*'] ); ``` ### bq First, use the [`bq mkdef`](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#bq_mkdef) command to create a table definition file: ```bash bq mkdef \ --source_format=SOURCE_FORMAT \ --connection_id=REGION.CONNECTION_ID \ --hive_partitioning_mode=PARTITIONING_MODE \ --hive_partitioning_source_uri_prefix=URI_SHARED_PREFIX \ --require_hive_partition_filter=BOOLEAN \ URIS > DEFINITION_FILE ``` Replace the following: - `SOURCE_FORMAT`: the format of the external data source. For example, `CSV`. - `REGION`: the region that contains the connection---for example, `us`. - `CONNECTION_ID`: the name of the connection---for example, `myconnection`. - `PARTITIONING_MODE`: the Hive partitioning mode. Use one of the following values: - `AUTO`: Automatically detect the key names and types. - `STRINGS`: Automatically convert the key names to strings. - `CUSTOM`: Encode the key schema in the source URI prefix. - `URI_SHARED_PREFIX`: the source URI prefix. - `BOOLEAN`: specifies whether to require a predicate filter at query time. This flag is optional. The default value is `false`. - `URIS`: the path to the Amazon S3 or the Blob Storage folder, using wildcard format. - `DEFINITION_FILE`: the path to the [table definition file](https://docs.cloud.google.com/bigquery/external-table-definition) on your local machine. If `PARTITIONING_MODE` is `CUSTOM`, include the partition key schema in the source URI prefix, using the following format: ```bash --hive_partitioning_source_uri_prefix=GCS_URI_SHARED_PREFIX/{KEY1:TYPE1}/{KEY2:TYPE2}/... ``` After you create the table definition file, use the [`bq mk`](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#mk-table) command to create the BigLake table: ```bash bq mk --external_table_definition=DEFINITION_FILE \ DATASET_NAME.TABLE_NAME \ SCHEMA ``` Replace the following: - `DEFINITION_FILE`: the path to the table definition file. - `DATASET_NAME`: the name of the dataset that contains the table. - `TABLE_NAME`: the name of the table you're creating. - `SCHEMA`: specifies a path to a [JSON schema file](https://docs.cloud.google.com/bigquery/docs/schemas#specifying_a_json_schema_file), or specifies the schema in the form `field:data_type,field:data_type,...`. To use schema auto-detection, omit this argument. **Examples** The following example uses `AUTO` Hive partitioning mode for Amazon S3 data: bq mkdef --source_format=CSV \ --connection_id=us.my-connection \ --hive_partitioning_mode=AUTO \ --hive_partitioning_source_uri_prefix=s3://mybucket/myTable \ --metadata_cache_mode=AUTOMATIC \ s3://mybucket/* > mytable_def bq mk --external_table_definition=mytable_def \ mydataset.mytable \ Region:STRING,Quarter:STRING,Total_sales:INTEGER The following example uses `STRING` Hive partitioning mode for Amazon S3 data: bq mkdef --source_format=CSV \ --connection_id=us.my-connection \ --hive_partitioning_mode=STRING \ --hive_partitioning_source_uri_prefix=s3://mybucket/myTable \ s3://mybucket/myTable/* > mytable_def bq mk --external_table_definition=mytable_def \ mydataset.mytable \ Region:STRING,Quarter:STRING,Total_sales:INTEGER The following example uses `CUSTOM` Hive partitioning mode for Blob Storage data: bq mkdef --source_format=CSV \ --connection_id=us.my-connection \ --hive_partitioning_mode=CUSTOM \ --hive_partitioning_source_uri_prefix=azure://mystorageaccount.blob.core.windows.net/mycontainer/{dt:DATE}/{val:STRING} \ azure://mystorageaccount.blob.core.windows.net/mycontainer/* > mytable_def bq mk --external_table_definition=mytable_def \ mydataset.mytable \ Region:STRING,Quarter:STRING,Total_sales:INTEGER ### API To set Hive partitioning using the BigQuery API, include the [`hivePartitioningOptions`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables#hivepartitioningoptions) object in the [`ExternalDataConfiguration`](https://docs.cloud.google.com/bigquery/docs/reference/rest/v2/tables#externaldataconfiguration) object when you create the [table definition file](https://docs.cloud.google.com/bigquery/external-table-definition). To create a BigLake table, you must also specify a value for the `connectionId` field. If you set the `hivePartitioningOptions.mode` field to `CUSTOM`, you must encode the partition key schema in the `hivePartitioningOptions.sourceUriPrefix` field as follows: `s3://BUCKET/PATH_TO_TABLE/{KEY1:TYPE1}/{KEY2:TYPE2}/...` To enforce the use of a predicate filter at query time, set the `hivePartitioningOptions.requirePartitionFilter` field to `true`. ## Delta Lake tables Delta Lake is an open source table format that supports petabyte scale data tables. Delta Lake tables can be queried as both temporary and permanent tables, and is supported as a [BigLake table](https://docs.cloud.google.com/bigquery/docs/biglake-intro). ### Schema synchronization Delta Lake maintains a canonical schema as part of its metadata. You can't update a schema using a JSON metadata file. To update the schema: 1. Use the [`bq update` command](https://docs.cloud.google.com/bigquery/docs/reference/bq-cli-reference#bq_update) with the `--autodetect_schema` flag: ```sh bq update --autodetect_schema PROJECT_ID:DATASET.TABLE ``` Replace the following: - `PROJECT_ID`: the project ID containing the table that you want to update - `DATASET`: the dataset containing the table that you want to update - `TABLE`: the table that you want to update ### Type conversion BigQuery converts Delta Lake data types to the following BigQuery data types: | **Delta Lake Type** | **BigQuery Type** | |---|---| | `boolean` | `BOOL` | | `byte` | `INT64` | | `int` | `INT64` | | `long` | `INT64` | | `float` | `FLOAT64` | | `double` | `FLOAT64` | | `Decimal(P/S)` | `NUMERIC` or `BIG_NUMERIC` depending on precision | | `date` | `DATE` | | `time` | `TIME` | | `timestamp (not partition column)` | `TIMESTAMP` | | `timestamp (partition column)` | `DATETIME` | | `string` | `STRING` | | `binary` | `BYTES` | | `array` | `ARRAY` | | `struct` | `STRUCT` | | `map` | `ARRAY>` | ### Limitations The following limitations apply to Delta Lake tables: - [External table limitations](https://docs.cloud.google.com/bigquery/docs/external-tables#limitations) apply to Delta Lake tables. - Delta Lake tables are only supported on BigQuery Omni and have the associated [limitations](https://docs.cloud.google.com/bigquery/docs/omni-introduction#limitations). - You can't update a table with a new JSON metadata file. You must use an auto detect schema table update operation. See [Schema synchronization](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-external-table#schema_synchronization) for more information. - BigLake security features only protect Delta Lake tables when accessed through BigQuery services. ### Create a Delta Lake table The following example creates an external table by using the [`CREATE EXTERNAL TABLE`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-definition-language#create_external_table_statement) statement with the Delta Lake format: ```googlesql CREATE [OR REPLACE] EXTERNAL TABLE table_name WITH CONNECTION connection_name OPTIONS ( format = 'DELTA_LAKE', uris = ["parent_directory"] ); ``` Replace the following: - table_name: The name of the table. - connection_name: The name of the connection. The connection must identify either an [Amazon S3](https://docs.cloud.google.com/bigquery/docs/omni-aws-create-connection) or a [Blob Storage](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-connection) source. - parent_directory: The URI of the parent directory. ### BigQuery Omni transfer with Delta Lake The following example uses the [`LOAD DATA`](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/other-statements#load_data_statement) statement to load data to the appropriate table: ```googlesql LOAD DATA [INTO | OVERWRITE] table_name FROM FILES ( format = 'DELTA_LAKE', uris = ["parent_directory"] ) WITH CONNECTION connection_name; ``` For more examples of BigQuery Omni data transfers, see [Load data with BigQuery Omni operations](https://docs.cloud.google.com/bigquery/docs/load-data-using-cross-cloud-transfer#load-data). ## Query BigLake tables For more information, see [Query Blob Storage data](https://docs.cloud.google.com/bigquery/docs/query-azure-data). ## View resource metadata with `INFORMATION_SCHEMA` > [!WARNING] > > **Preview** > > > This feature is > > subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the > [Service Specific > Terms](https://docs.cloud.google.com/terms/service-terms#1). > > Pre-GA features are available "as is" and might have limited support. > > For more information, see the > [launch stage descriptions](https://cloud.google.com/products/#product-launch-stages). You can view the resource metadata with [`INFORMATION_SCHEMA`](https://docs.cloud.google.com/bigquery/docs/information-schema-intro) views. When you query the [`JOBS_BY_*`](https://docs.cloud.google.com/bigquery/docs/information-schema-jobs), [`JOBS_TIMELINE_BY_*`](https://docs.cloud.google.com/bigquery/docs/information-schema-jobs-timeline), and [`RESERVATION*`](https://docs.cloud.google.com/bigquery/docs/information-schema-reservations) views, you must [specify the query's processing location](https://docs.cloud.google.com/bigquery/docs/locations#specify_locations) that is collocated with the table's region. For information about BigQuery Omni locations, see [Locations](https://docs.cloud.google.com/bigquery/docs/locations#omni-loc). For all other system tables, specifying the query job location is *optional*. For information about the system tables that BigQuery Omni supports, see [Limitations](https://docs.cloud.google.com/bigquery/docs/omni-introduction#limitations). To query `JOBS_*` and `RESERVATION*` system tables, select one of the following methods to specify the processing location: ### Console 1. Go to the **BigQuery** page. [Go to BigQuery](https://console.cloud.google.com/bigquery) 2. If the **Editor** tab isn't visible, then click **Compose new query**. 3. Click **Edit** \> **Query settings** . The **Query settings** dialog opens. 4. In the **Query settings** dialog, for **Additional settings** \> **Data location** , select the [BigQuery region](https://docs.cloud.google.com/bigquery/docs/locations#omni-loc) that is collocated with the BigQuery Omni region. For example, if your BigQuery Omni region is `aws-us-east-1`, specify `us-east4`. 5. Select the remaining fields and click **Save**. ### bq Use the `--location` flag to set the job's processing location to the [BigQuery region](https://docs.cloud.google.com/bigquery/docs/locations#omni-loc) that is collocated with the BigQuery Omni region. For example, if your BigQuery Omni region is `aws-us-east-1`, specify `us-east4`. **Example** bq query --use_legacy_sql=false --location=us-east4 \ "SELECT * FROM region-azure-eastus2.INFORMATION_SCHEMA.JOBS limit 10;" ### API If you are [running jobs programmatically](https://docs.cloud.google.com/bigquery/docs/running-jobs), set the location argument to the [BigQuery region](https://docs.cloud.google.com/bigquery/docs/locations#omni-loc) that is collocated with the BigQuery Omni region. For example, if your BigQuery Omni region is `aws-us-east-1`, specify `us-east4`. ## VPC Service Controls You can use VPC Service Controls perimeters to restrict access from BigQuery Omni to an external cloud service as an extra layer of defense. For example, VPC Service Controls perimeters can limit exports from your BigQuery Omni tables to a specific Amazon S3 bucket or Blob Storage container. To learn more about VPC Service Controls, see [Overview of VPC Service Controls](https://docs.cloud.google.com/vpc-service-controls/docs/overview). ### Required permission Ensure that you have the required permissions to configure service perimeters. To view a list of IAM roles required to configure VPC Service Controls, see [Access control with IAM](https://docs.cloud.google.com/vpc-service-controls/docs/access-control) in the VPC Service Controls documentation. ### Set up VPC Service Controls using the Google Cloud console 1. In the Google Cloud console navigation menu, click **Security** , and then click **VPC Service Controls**. [Go to VPC Service Controls](https://console.cloud.google.com/security/service-perimeter) 2. To set up VPC Service Controls for BigQuery Omni, follow the steps in the [Create a service perimeter](https://docs.cloud.google.com/vpc-service-controls/docs/create-service-perimeters#create_a_service_perimeter) guide, and when you are in the **Egress rules** pane, follow these steps: 1. In the **Egress rules** panel, click **Add rule**. 2. In the **From attributes of the API client** section, select an option from the **Identity** list. 3. Select **To attributes of external resources**. 4. To add an external resource, click **Add external resources**. 5. In the **Add external resource** dialog, for **External resource name**, enter a valid resource name. For example: - For Amazon Simple Storage Service (Amazon S3): `s3://BUCKET_NAME` Replace BUCKET_NAME with the name of your Amazon S3 bucket. - For Azure Blob Storage: `azure://myaccount.blob.core.windows.net/CONTAINER_NAME` Replace CONTAINER NAME with the name of your Blob Storage container. For a list of egress rule attributes, see [Egress rules reference](https://docs.cloud.google.com/vpc-service-controls/docs/ingress-egress-rules#egress_rules_reference). 6. Select the methods that you want to allow on your external resources: 1. If you want to allow all methods, select **All methods** in the **Methods** list. 2. If you want to allow specific methods, select **Selected method** , click **Select methods**, and then select the methods that you want to allow on your external resources. 7. Click **Create perimeter**. ### Set up VPC Service Controls using the gcloud CLI To set up VPC Service Controls using the gcloud CLI, follow these steps: 1. [Set the default access policy](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-external-table#set-default-policy). 2. [Create the egress policy input file](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-external-table#create-egress-file). 3. [Add the egress policy](https://docs.cloud.google.com/bigquery/docs/omni-azure-create-external-table#add-egress-policy). #### Set the default access policy An access policy is an organization-wide container for access levels and service perimeters. For information about setting a default access policy or getting an access policy name, see [Managing an access policy](https://docs.cloud.google.com/access-context-manager/docs/manage-access-policy#set-default). #### Create the egress policy input file An egress rule block defines the allowed access from within a perimeter to resources outside of that perimeter. For external resources, the `externalResources` property defines the external resource paths allowed access from within your VPC Service Controls perimeter. Egress rules can be configured using a JSON file, or a YAML file. The following sample uses the `.yaml` format: ```yaml - egressTo: operations: - serviceName: bigquery.googleapis.com methodSelectors: - method: "*" *OR* - permission: "externalResource.read" externalResources: - EXTERNAL_RESOURCE_PATH egressFrom: identityType: IDENTITY_TYPE *OR* identities: - serviceAccount:SERVICE_ACCOUNT ``` - `egressTo`: lists allowed service operations on Google Cloud resources in specified projects outside the perimeter. - `operations`: list accessible services and actions or methods that a client satisfying the `from` block conditions is allowed to access. - `serviceName`: set `bigquery.googleapis.com` for BigQuery Omni. - `methodSelectors`: list methods that a client satisfying the `from` conditions can access. For restrictable methods and permissions for services, see [Supported service method restrictions](https://docs.cloud.google.com/vpc-service-controls/docs/supported-method-restrictions). - `method` : a valid service method, or `\"*\"` to allow all `serviceName` methods. - `permission`: a valid service permission, such as `\"*\"`, `externalResource.read`, or `externalResource.write`. Access to resources outside the perimeter is allowed for operations that require this permission. - `externalResources`: lists external resources that clients inside a perimeter can access. Replace EXTERNAL_RESOURCE_PATH with either a valid Amazon S3 bucket, such as `s3://bucket_name`, or a Blob Storage container path, such as `azure://myaccount.blob.core.windows.net/container_name`. - `egressFrom`: lists allowed service operations on Google Cloud resources in specified projects within the perimeter. - `identityType` or `identities`: defines the identity types that can access the specified resources outside the perimeter. Replace IDENTITY_TYPE with one of the following valid values: - `ANY_IDENTITY`: to allow all identities. - `ANY_USER_ACCOUNT`: to allow all users. - `ANY_SERVICE_ACCOUNT`: to allow all service accounts - `identities`: lists service accounts that can access the specified resources outside the perimeter. - `serviceAccount` (optional): replace SERVICE_ACCOUNT with the service account that can access the specified resources outside the perimeter. #### Examples The following example is a policy that allows egress operations from inside the perimeter to the `s3://mybucket` Amazon S3 location in AWS. ```yaml - egressTo: operations: - serviceName: bigquery.googleapis.com methodSelectors: - method: "*" externalResources: - s3://mybucket - s3://mybucket2 egressFrom: identityType: ANY_IDENTITY ``` The following example allows egress operations to a Blob Storage container: ```yaml - egressTo: operations: - serviceName: bigquery.googleapis.com methodSelectors: - method: "*" externalResources: - azure://myaccount.blob.core.windows.net/mycontainer egressFrom: identityType: ANY_IDENTITY ``` For more information about egress policies, see the [Egress rules reference](https://docs.cloud.google.com/vpc-service-controls/docs/ingress-egress-rules#egress-rules-reference). #### Add the egress policy To add the egress policy when you create a new service perimeter, use the [`gcloud access-context-manager perimeters create` command](https://docs.cloud.google.com/sdk/gcloud/reference/access-context-manager/perimeters/create). For example, the following command creates a new perimeter named `omniPerimeter` that includes the project with project number `12345`, restricts the BigQuery API, and adds an egress policy defined in the `egress.yaml` file: ```bash gcloud access-context-manager perimeters create omniPerimeter \ --title="Omni Perimeter" \ --resources=projects/12345 \ --restricted-services=bigquery.googleapis.com \ --egress-policies=egress.yaml ``` To add the egress policy to an existing service perimeter, use the [`gcloud access-context-manager perimeters update` command](https://docs.cloud.google.com/sdk/gcloud/reference/access-context-manager/perimeters/update). For example, the following command adds an egress policy defined in the `egress.yaml` file to an existing service perimeter named `omniPerimeter`: ```bash gcloud access-context-manager perimeters update omniPerimeter --set-egress-policies=egress.yaml ``` ### Verify your perimeter To verify the perimeter, use the [`gcloud access-context-manager perimeters describe` command](https://docs.cloud.google.com/sdk/gcloud/reference/access-context-manager/perimeters/describe): ```bash gcloud access-context-manager perimeters describe PERIMETER_NAME ``` Replace PERIMETER_NAME with the name of the perimeter. For example, the following command describes the perimeter `omniPerimeter`: ```bash gcloud access-context-manager perimeters describe omniPerimeter ``` For more information, see [Managing service perimeters](https://docs.cloud.google.com/vpc-service-controls/docs/manage-service-perimeters#list-and-describe). ## Allow BigQuery Omni VPC access to Blob Storage As a BigQuery administrator, you can create a network rule to grant BigQuery Omni access to your Blob Storage resources. This ensures that only authorized BigQuery Omni VPCs can interact with your Blob Storage, enhancing the security of your data. ### Apply a network rule for BigQuery Omni VPC To apply a network rule, use the Azure PowerShell or Terraform: ### Azure PowerShell Run the following command to add a network rule to your storage account that specifies the retrieved BigQuery Omni subnet IDs as the `VirtualNetworkResourceId`. ```bash Add-AzStorageAccountNetworkRule` -ResourceGroupName "RESOURCE_GROUP_NAME"` -Name "STORAGE_ACCOUNT_NAME"` -VirtualNetworkResourceId "SUBNET_ID1","SUBNET_ID2" ``` Replace the following: - `RESOURCE_GROUP_NAME`: the resource group name. - `STORAGE_ACCOUNT_NAME`: the storage account name. - `SUBNET_ID1`,`SUBNET_ID1`: the subnet IDs. You can find this information in the table on this page. ### Terraform Add the following to your Terraform configuration file: ```terraform resource "azurerm_storage_account_network_rules" "example" { storage_account_name = "STORAGE_ACCOUNT_NAME" resource_group_name = "RESOURCE_GROUP_NAME" default_action = "Allow" bypass = ["Logging", "Metrics", "AzureServices"] virtual_network_subnet_ids = ["SUBNET_ID1","SUBNET_ID2"] } ``` Replace the following: - `STORAGE_ACCOUNT_NAME`: the storage account name. - `RESOURCE_GROUP_NAME`: the resource group name. - `SUBNET_ID1`,`SUBNET_ID1`: the subnet IDs. You can find this information in the table on this page. ### BigQuery Omni VPC Resource IDs | **Region** | **Subnet IDs** | |---|---| | azure-eastus2 | /subscriptions/95f30708-58d1-48ba-beac-d71870c3b2f5/resourceGroups/bqe-prod-eastus2-resource-group/providers/Microsoft.Network/virtualNetworks/bqe-prod-eastus2-network/subnets/azure-prod-eastus21-yurduaaaaa-private /subscriptions/95f30708-58d1-48ba-beac-d71870c3b2f5/resourceGroups/bqe-prod-eastus2-resource-group/providers/Microsoft.Network/virtualNetworks/bqe-prod-eastus2-network/subnets/azure-prod-eastus22-yurduaaaab-private | ## Limitations For a full list of limitations that apply to BigLake tables based on Amazon S3 and Blob Storage, see [Limitations](https://docs.cloud.google.com/bigquery/docs/omni-introduction#limitations). ## What's next - Learn about [BigQuery Omni](https://docs.cloud.google.com/bigquery/docs/omni-introduction). - Learn about [BigLake tables](https://docs.cloud.google.com/bigquery/docs/biglake-intro). - Learn how to [export query results to Blob Storage](https://docs.cloud.google.com/bigquery/docs/omni-azure-export-results-to-azure-storage).