Create Blob Storage BigLake tables

This document describes how to create an Azure Blob Storage BigLake table. A BigLake table lets you use access delegation to query data in Blob Storage. Access delegation decouples access to the BigLake table from access to the underlying datastore.

For information about how data flows between BigQuery and Blob Storage, see Data flow when querying data.

Before you begin

Ensure that you have a connection to access data in your Blob Storage.

Required roles

To get the permissions that you need to create an external table, ask your administrator to grant you the BigQuery Admin (roles/bigquery.admin) IAM role on your dataset. For more information about granting roles, see Manage access to projects, folders, and organizations.

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 or other predefined roles.

Create a dataset

Before you create an external table, you need to create a dataset in the supported region. Select one of the following options:

Console

1. Go to the BigQuery page.

   Go to BigQuery

2. In the Explorer pane, select the project where you want to create the dataset.
3. Expand the View actions option and click Create dataset.
4. On the Create dataset page, specify the following details:
1. For Dataset ID enter a unique dataset name.
2. For Data location choose a supported region.
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, expand the Advanced options section and then select the Enable default collation option.
5. Click Create dataset.

SQL

Use the CREATE SCHEMA DDL 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

2. In the query editor, enter the following statement:

   CREATE SCHEMA mydataset
   OPTIONS (
     location = 'azure-eastus2');

3. Click Run.

For more information about how to run queries, see Run an interactive query.

bq

In a command-line environment, create a dataset using the bq mk command:

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. 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.

• 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

2. In the Explorer pane, expand your project, and then select a dataset.

3. In the Dataset info section, click Create table.

4. 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.

5. 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.

6. In the Schema section, you can either enable schema auto-detection 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.

7. Click Create table.

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 statement with the WITH CONNECTION clause:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   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, such as AVRO, CSV, DELTA_LAKE, or ICEBERG (preview)
   • 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.

Example:

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:

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, 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:

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:

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: 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 API method, and create an ExternalDataConfiguration in the Table resource 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

2. In the Explorer pane, expand your project and select a dataset.

3. Click View actions, and then click Create table. This opens the Create table pane.

4. 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. 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.

      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.

5. 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.

6. In the Schema section, you can either enable schema auto-detection 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.

7. To ignore rows with extra column values that don't match the schema, expand the Advanced options section and select Unknown values.

8. Click Create table.

SQL

Use the CREATE EXTERNAL TABLE DDL statement:

1. In the Google Cloud console, go to the BigQuery page.

   Go to BigQuery

2. In the query editor, enter the following statement:

   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.

Examples

The following example creates a BigLake table over partitioned data in Amazon S3. The schema is autodetected.

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.

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 command to create a table definition file:

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 on your local machine.

If PARTITIONING_MODE is CUSTOM, include the partition key schema in the source URI prefix, using the following format:

--hive_partitioning_source_uri_prefix=GCS_URI_SHARED_PREFIX/{KEY1:TYPE1}/{KEY2:TYPE2}/...

After you create the table definition file, use the bq mk command to create the BigLake table:

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, 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 object in the ExternalDataConfiguration object when you create the table definition file. 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.

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 with the --autodetect_schema flag:

   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<Type>	ARRAY<Type>
struct	STRUCT
map<KeyType, ValueType>	ARRAY<Struct<key KeyType, value ValueType>>

Limitations

The following limitations apply to Delta Lake tables:

• External table limitations apply to Delta Lake tables.

• Delta Lake tables are only supported on BigQuery Omni and have the associated 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 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 statement with the Delta Lake format:

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 or a Blob Storage source.

• parent_directory: The URI of the parent directory.

Cross-cloud transfer with Delta Lake

The following example uses the LOAD DATA statement to load data to the appropriate table:

LOAD DATA [INTO | OVERWRITE] table_name
FROM FILES (
        format = 'DELTA_LAKE',
        uris = ["parent_directory"]
)
WITH CONNECTION connection_name;

For more examples of cross-cloud data transfers, see Load data with cross cloud operations.

Query BigLake tables

For more information, see Query Blob Storage data.

View resource metadata with INFORMATION_SCHEMA

Preview

This feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the Service Specific Terms. Pre-GA features are available "as is" and might have limited support. For more information, see the launch stage descriptions.

You can view the resource metadata with INFORMATION_SCHEMA views. When you query the JOBS_BY_*, JOBS_TIMELINE_BY_*, and RESERVATION* views, you must specify the query's processing location that is collocated with the table's region. For information about BigQuery Omni locations, see Locations. For all other system tables, specifying the query job location is optional.

For information about the system tables that BigQuery Omni supports, see 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

2. If the Editor tab isn't visible, then click Compose new query.

3. Click More > Query settings. The Query settings dialog opens.

4. In the Query settings dialog, for Additional settings > Data location, select the BigQuery region 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 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, set the location argument to the BigQuery region 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.

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 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

2. To set up VPC Service Controls for BigQuery Omni, follow the steps in the 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.

   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.
2. Create the egress policy input file.
3. Add the 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.

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:

- 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.

• 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.

- 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:

- 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.

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. 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:

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. For example, the following command adds an egress policy defined in the egress.yaml file to an existing service perimeter named omniPerimeter:

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:

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:

gcloud access-context-manager perimeters describe omniPerimeter

For more information, see Managing service perimeters.

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.

  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:

  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.

What's next

• Learn about BigQuery Omni.
• Learn about BigLake tables.
• Learn how to export query results to Blob Storage.