Create cross-cloud connections

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Cross-cloud connections are BigQuery connections that bring data from Amazon Web Services (AWS), Microsoft Azure, and Salesforce Data 360 into BigQuery for querying. These connections are an alternative to standard BigQuery connections that use BigQuery Omni, where BigQuery deploys lightweight compute workers to remote regions to make data in other clouds available for querying without any data movement.

Workloads can benefit from cross-cloud connections versus standard connections for the following reasons:

• Feature consistency. By bringing your data from other clouds into BigQuery, you gain direct access to BigQuery AI capabilities, Gemini Enterprise Agent Platform, materialized views, user-defined functions, and other features that aren't available through standard connections.
• Cost efficiency. Workloads that use cross-cloud connections consume standard slot reservations and commitments, eliminating the need to manage separate compute capacities.

If your workload involves Apache Iceberg catalogs or you need query support across engines other than BigQuery, consider using cross-cloud Lakehouse for Apache Iceberg instead.

Before you begin

Grant Identity and Access Management and third-party roles that give users the necessary permissions to perform each task in this document.

Required BigQuery roles

To get the permissions that you need to create a cross-cloud connection, ask your administrator to grant you the BigQuery Admin (roles/bigquery.admin) IAM role on the project. For more information about granting roles, see Manage access to projects, folders, and organizations.

You might also be able to get the required permissions through custom roles or other predefined roles.

Required third-party roles

To get the permissions that you need to create a cross-cloud connection, ensure that you have the following third-party roles:

• For AWS: a role that lets you create IAM policies and roles in your AWS account.
• For Azure: a role that lets you manage App Registrations in Microsoft Entra ID (Azure AD) and assign roles, such as Storage Blob Data Reader, on the target Azure account.

Create AWS cross-cloud connections

To create an AWS cross-cloud connection, do the following:

1. Create an AWS IAM policy for BigQuery as you would for standard BigQuery connections.
2. Create an AWS IAM role for BigQuery as you would for standard BigQuery connections.

3. To create a connection resource, use the bq mk --connection command:

   bq mk --connection \
       --connection_type='AWS' \
       --location=LOCATION \
       --project_id=PROJECT_ID \
       --properties='{"accessRole":{"iamRoleId":"arn:aws:iam::AWS_ACCOUNT_ID:role/ROLE_NAME"}}' \
       CONNECTION_ID

   Replace the following:

   • LOCATION: a standard BigQuery location, not a BigQuery Omni location. For guidance on selecting a region, see Region recommendations.
   • PROJECT_ID: the ID of your Google Cloud project.
   • AWS_ACCOUNT_ID: the ID of the AWS IAM user from the previous step.
   • ROLE_NAME: the AWS role policy name that you chose.
   • CONNECTION_ID: an ID to give to this connection resource.

4. To note the service account information, use the bq show command:

   bq show --connection \
       --location=LOCATION \
       PROJECT_ID.LOCATION.CONNECTION_ID

5. Add a trust policy to the AWS role using the service account information, as you would for standard BigQuery connections.

With your established AWS cross-cloud connection, you can now create tables and datasets to query your remote data.

AWS examples

The following example creates an external table to query Parquet files directly from an Amazon Simple Storage Service (Amazon S3) bucket with a cross-cloud connection:

CREATE SCHEMA `my-project.aws_raw_data`
  OPTIONS (
    location = 'us-east4');

CREATE EXTERNAL TABLE `my-project.aws_raw_data.sales_parquet`
WITH CONNECTION `us-east4.my-aws-connection`
  OPTIONS (
    format = 'PARQUET',
    uris = ['s3://my-data-bucket/sales/year=2025/*']);

The following example federates an entire database into BigQuery using AWS Glue with a cross-cloud connection:

CREATE EXTERNAL SCHEMA `my-project.aws_glue_data`
WITH CONNECTION `us-east4.my-aws-connection`
  OPTIONS (
    location = 'us-east4',
    external_source = 'aws-glue://arn:aws:glue:us-east-4:123456789:database/test_database');

For more information on AWS Glue federation, see Create and manage AWS Glue federated datasets.

Create Azure cross-cloud connections

To create an Azure cross-cloud connection, do the following:

1. Create an application in your Azure tenant as you would for standard BigQuery connections, and take note of the Application (client) and Directory (tenant) IDs.
2. Assign a role to the Azure application as you would for standard BigQuery connections.

3. To create a connection resource, use the bq mk --connection command:

   bq mk --connection \
       --connection_type='Azure' \
       --tenant_id=TENANT_ID \
       --location=LOCATION \
       --federated_azure=true \
       --federated_app_client_id=APP_ID \
       --project_id=PROJECT_ID \
       CONNECTION_ID

   Replace the following:

   • TENANT_ID: the tenant ID of the Azure directory that contains the Azure account.
   • LOCATION: a standard BigQuery location, not a BigQuery Omni location. For guidance on selecting a region, see Region recommendations.
   • APP_ID: the Azure Application (client) ID.
   • PROJECT_ID: the ID of your Google Cloud project.
   • CONNECTION_ID: an ID to give to this connection resource.

4. To note the service account information, use the bq show command:

   bq show --connection \
       --location=LOCATION \
       PROJECT_ID.LOCATION.CONNECTION_ID

5. Add a federated credential using the service account information, as you would for standard BigQuery connections.

With your established Azure cross-cloud connection, you can now create tables and datasets to query your remote data.

Azure examples

The following example creates an external table to query Parquet files directly from an Azure Blob Storage container with a cross-cloud connection:

CREATE SCHEMA `my-project.azure_raw_data`
  OPTIONS (
    location = 'us-east4');

CREATE EXTERNAL TABLE `my-project.azure_raw_data.sales_parquet`
WITH CONNECTION `us-east4.my-azure-connection`
  OPTIONS (
    format = 'PARQUET',
    uris = ['azure://mystorageaccount.blob.core.windows.net/mycontainer/sales/year=2025/*']);

The following example creates an external table to query raw CSV exports from a Blob Storage container with a cross-cloud connection:

CREATE EXTERNAL TABLE `my-project.azure_raw_data.daily_logs_csv`
WITH CONNECTION `us-east4.my-azure-connection`
  OPTIONS (
    format = 'CSV',
    skip_leading_rows = 1,
    uris = ['azure://mystorageaccount.blob.core.windows.net/mycontainer/logs/*.csv']);

Create Data 360 cross-cloud connections

To create a Data 360 cross-cloud connection, follow the steps to link a Data 360 dataset to BigQuery, except instead of creating the linked dataset in a BigQuery Omni location, create it in a standard BigQuery location. For guidance on selecting a region, see Region recommendations.

After you update your queries to use the new datasets, you can delete any legacy linked datasets and BigQuery Omni materialized views.

Region recommendations

When you select a standard BigQuery region for your cross-cloud connection, choose the region that is physically closest to your data for optimal cost and performance efficiency.

The following table lists AWS regions and the best corresponding BigQuery regions:

AWS region	Closest BigQuery region	Other close BigQuery regions
us-east-1	us-east4	us-east1 us-east5 us-central1
us-east-2	us-east5	us-east4 us-east1 us-central1
us-west-1	us-west2	us-west4 us-west1 us-west3
us-west-2	us-west1	us-west3 us-west4 us-west2
ca-central-1	northamerica-northeast1	northamerica-northeast2 us-east4 us-east5
sa-east-1	southamerica-east1	southamerica-west1 us-east1 us-south1
eu-west-1	europe-west1	europe-west2 europe-west9 europe-west4
eu-west-2	europe-west2	europe-west1 europe-west9 europe-west4
eu-west-3	europe-west9	europe-west1 europe-west2 europe-west3
eu-central-1	europe-west3	europe-west4 europe-west1 europe-west6
eu-central-2	europe-west6	europe-west8 europe-west12 europe-west3
eu-north-1	europe-north1	europe-west4 europe-central2 europe-west1
eu-south-1	europe-west8	europe-west12 europe-west6 europe-west3
eu-south-2	europe-southwest1	europe-west9 europe-west1 europe-west8
me-central-1	me-central1	me-central2 me-west1 asia-south1
me-south-1	me-central2	me-central1 me-west1 asia-south1
af-south-1	europe-southwest1	me-central2 me-west1 europe-west1
ap-east-1	asia-east2	asia-east1 asia-southeast1 asia-northeast1
ap-northeast-1	asia-northeast1	asia-northeast2 asia-northeast3 asia-east1
ap-northeast-2	asia-northeast3	asia-northeast2 asia-northeast1 asia-east1
ap-northeast-3	asia-northeast2	asia-northeast1 asia-northeast3 asia-east1
ap-south-1	asia-south1	asia-south2 me-central1 asia-southeast1
ap-south-2	asia-south2	asia-south1 asia-southeast1 me-central1
ap-southeast-1	asia-southeast1	asia-southeast2 asia-east1 asia-south1
ap-southeast-2	australia-southeast1	australia-southeast2 asia-southeast2 asia-southeast1
ap-southeast-3	asia-southeast2	asia-southeast1 australia-southeast1 asia-east1

The following table lists Azure regions and the best corresponding BigQuery regions:

Azure region	Closest BigQuery region	Other close BigQuery regions
East US	us-east4	us-east1 us-east5 us-central1
East US 2	us-east4	us-east1 us-east5 us-south1
West US	us-west1	us-west2 us-west3 us-west4
West US 2	us-west1	us-west4 us-west3 us-west2
West US 3	us-west4	us-west2 us-west3 us-west1
Central US	us-central1	us-east5 us-east4 us-east1
North Central US	us-east5	us-central1 us-east4 us-east1
South Central US	us-south1	us-east5 us-central1 us-east1
West Central US	us-west3	us-central1 us-west4 us-west2
Canada Central	northamerica-northeast2	northamerica-northeast1 us-east5 us-east4
Canada East	northamerica-northeast1	northamerica-northeast2 us-east4 us-east5
West Europe	europe-west4	europe-west1 europe-west3 europe-west9
North Europe	europe-west1	europe-west2 europe-west9 europe-west4
France Central	europe-west9	europe-west1 europe-west2 europe-west3
France South	europe-west9	europe-southwest1 europe-west8 europe-west1
Germany West Central	europe-west3	europe-west4 europe-west1 europe-west6
Germany North	europe-west3	europe-west4 europe-central2 europe-west2
Switzerland North	europe-west6	europe-west8 europe-west12 europe-west3
Switzerland West	europe-west6	europe-west9 europe-west8 europe-west3
UK South	europe-west2	europe-west1 europe-west9 europe-west4
UK West	europe-west2	europe-west1 europe-west9 europe-west4
Norway East	europe-north1	europe-west4 europe-central2 europe-west1
Norway West	europe-north1	europe-west4 europe-central2 europe-west1
Sweden Central	europe-north1	europe-west4 europe-central2 europe-west1
East Asia	asia-east2	asia-east1 asia-southeast1 asia-northeast1
Southeast Asia	asia-southeast1	asia-southeast2 asia-east1 asia-south1
Japan East	asia-northeast1	asia-northeast2 asia-northeast3 asia-east1
Japan West	asia-northeast2	asia-northeast1 asia-northeast3 asia-east1
Korea Central	asia-northeast3	asia-northeast2 asia-northeast1 asia-east1
Australia East	australia-southeast1	australia-southeast2 asia-southeast2 asia-southeast1
Australia Southeast	australia-southeast2	australia-southeast1 asia-southeast1 asia-east1
Australia Central	australia-southeast1	australia-southeast2 asia-southeast2 asia-southeast1
West India	asia-south1	asia-south2 me-central1 asia-southeast1
Central India	asia-south1	asia-south2 me-central1 asia-southeast1
South India	asia-south1	asia-south2 asia-southeast1 me-central1

What's next

• Learn about BigQuery analytics.