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BigQuery locations | Google Cloud

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This page explains the concept of location and the different regions where data can be stored and processed. Pricing for storage and analysis is also defined by location of data and reservations. For more information about pricing for locations, see BigQuery pricing. To learn how to set the location for your dataset, see Create datasets. For information about reservation locations, see Managing reservations in different regions.

For more information about how the BigQuery Data Transfer Service uses location, see Data location and transfers.

BigQuery provides two types of data and compute locations:

• A region is a specific geographic place, such as London.

• A multi-region is a large geographic area, such as the United States, that contains two or more regions. Multi-region locations can provide larger quotas than single regions.

For either location type, BigQuery automatically stores copies of your data in two different Google Cloud zones within a single region in the selected location. For more information about data availability and durability, see Disaster planning.

BigQuery datasets can be stored in the following regions and multi-regions. For more information about regions and zones, see Geography and regions.

The following table lists the regions in the Americas where BigQuery is available.

The following table lists the regions in Asia Pacific where BigQuery is available.

The following table lists the regions in Europe where BigQuery is available.

The following table lists the regions in the Middle East where BigQuery is available.

The following table lists the regions in Africa where BigQuery is available.

The following table lists the multi-regions where BigQuery is available.

¹ Data located in the EU multi-region is only stored in one of the following locations: europe-west1 (Belgium) or europe-west4 (Netherlands). The exact location in which the data is stored and processed is determined automatically by BigQuery.

² Data located in the US multi-region is only stored in one of the following locations: us-central1 (Iowa), us-west1 (Oregon), or us-central2 (Oklahoma). The exact location in which the data is stored and processed is determined automatically by BigQuery.

BigQuery Studio lets you save, share, and manage versions of code assets such as notebooks and saved queries.

The following table lists the regions where BigQuery Studio is available:

BigQuery Omni processes queries in the same location as the dataset that contains the tables you're querying. After you create the dataset, the location cannot be changed. Your data resides within your AWS or Azure account. BigQuery Omni regions support Enterprise edition reservations and on-demand compute (analysis) pricing. For more information about editions, see

.

The following sections describe supported locations for BigQuery ML models.

This section contains information about supported locations for

, and about where remote model processing occurs.

See the following documentation for supported locations for remote models over Google models and partner models:

• For Gemini model and embedding model supported regions, see Google model endpoint locations.
• For Claude, Llama, and Mistral AI model supported regions, see Google Cloud partner model endpoint locations.

The following table shows which regions are supported for remote models over Cloud AI services and custom models deployed to Vertex AI. The column name indicates the type of remote model.

If the dataset in which you are creating the remote model is in a single region, the Vertex AI model endpoint must be in the same region. If you specify the model endpoint URL, use the endpoint in the same region as the dataset. For example, if the dataset is in the us-central1 region, then specify the endpoint https://us-central1-aiplatform.googleapis.com/v1/projects/myproject/locations/us-central1/publishers/google/models/<target_model>. If you specify the model name, BigQuery ML automatically chooses the endpoint in the correct region.

Multi-regional support for remote models is as follows:

• Gemini models are supported in the US and EU multi-regions.
• Claude, Llama, and Mistral AI models in the US multi-region can use the Vertex AI endpoint for any single region within the US multi-region. Claude, Llama, and Mistral AI models in the EU multi-region can use the Vertex AI endpoint for any single region within the EU multi-region except for eu-west2 and eu-west6.
• Vertex AI deployed models aren't supported in either multi-region.
• Cloud AI services are supported in the US and EU multi-regions.

If the dataset in which you are creating the remote model is in a multi-region, then the Vertex AI model endpoint must be in a region within that multi-region. For example, if the dataset is in the eu multi-region, then you could specify the URL for the europe-west1 region endpoint, https://europe-west1-aiplatform.googleapis.com/v1/projects/myproject/locations/europe-west1/publishers/google/models/<target_model>. If you specify the model name instead of the endpoint URL, BigQuery ML defaults to using the europe-west4 endpoint for datasets in the eu multi-region, and to using the us-central1 endpoint for datasets in the us multi-region.

For information about processing locations used by Google models hosted in Vertex AI, see ML processing for Google Cloud models.

For information about processing locations used by partner models hosted in Vertex AI, see ML processing for Google Cloud partner models.

This section contains information about supported locations for

other than remote models, and about where model processing occurs.

The following table contains information about supported locations for all model types other than remote models:

All supported models other than remote models are supported in the US and EU multi-regions.

Data located in the EU multi-region is not stored in the europe-west2 (London) or europe-west6 (Zürich) data centers.

Vertex AI Model Registry integration is supported only for single region integrations. If you send a multi-region BigQuery ML model to the Model Registry, then it is converted to a regional model in Vertex AI. A BigQuery ML multi-region US model is synced to Vertex AI us-central1 and a BigQuery ML multi-region EU model is synced to Vertex AI europe-west4. For single region models, there are no changes.

For models other than remote models, BigQuery ML processes and stages data in the same location as the dataset that contains the data.

BigQuery ML stores your data in the selected location in accordance with the Service Specific Terms.

When migrating data from your legacy data warehouse into BigQuery, you can use several SQL translators to translate your SQL queries into GoogleSQL or other supported SQL dialects. These include the interactive SQL translator, the SQL translation API, and the batch SQL translator.

The BigQuery SQL translators are available in the following processing locations:

The following table lists the regions where continuous queries are supported:

The BigQuery partitioning and clustering recommender generates partition or cluster recommendations to optimize your BigQuery tables.

The partitioning and clustering recommender is available in the following processing locations:

BigQuery sharing (formerly Analytics Hub) is available in the following regions and multi-regions.

The following table lists the regions in the Americas where sharing is available.

The following table lists the regions in Asia Pacific where sharing is available.

The following table lists the regions in Europe where sharing is available.

The following table lists the regions in the Middle East where sharing is available.

The following table lists the regions in Africa where sharing is available.

The following table lists the multi-regions where sharing is available.

¹ Data located in the EU multi-region is not stored in the europe-west2 (London) or europe-west6 (Zürich) data centers.

The following table lists the Omni where sharing is available.

When loading data, querying data, or exporting data, BigQuery determines the location to run the job based on the datasets referenced in the request. For example, if a query references a table in a dataset stored in the asia-northeast1 region, the query job will run in that region.

If a query does not reference any tables or other resources contained within datasets, and no destination table is provided, the query job will run in the US multi-region. To ensure that BigQuery queries are stored in a specific region or multi-region, specify the location with the job request to route the query accordingly when using the global BigQuery endpoint. If you don't specify the location, queries may be temporarily stored in BigQuery router logs when the query is used for determining the processing location in BigQuery.

If the project has a capacity-based reservation in a region other than the US and the query does not reference any tables or other resources contained within datasets, then you must explicitly specify the location of the capacity-based reservation when submitting the job. Capacity-based commitments are tied to a location, such as US or EU. If you run a job outside the location of your capacity, pricing for that job automatically shifts to on-demand pricing.

You can specify the location to run a job explicitly in the following ways:

• When you query data using the Google Cloud console in the query editor, click settings More > Query settings, expand Advanced options, and then select your Data location.
• When you write a SQL query, set the @@location system variable in the first statement of your query.
• When you use the bq command-line tool, supply the --location global flag and set the value to your location.
• When you use the API, specify your region in the location property in the jobReference section of the job resource.

BigQuery returns an error if the specified location does not match the location of the datasets in the request. The location of every dataset involved in the request, including those read from and those written to, must match the location of the job as inferred or specified.

Single-region locations don't match multi-region locations, even where the single-region location is contained within the multi-region location. Therefore, a query or job will fail if the location includes both a single-region location and a multi-region location. For example, if a job's location is set to US, the job will fail if it references a dataset in us-central1. Likewise, a job that references one dataset in US and another dataset in us-central1 will fail. This is also true for JOIN statements with tables in both a region and a multi-region.

Dynamic queries aren't parsed until they execute, so they can't be used to automatically determine the region of a query.

Capacity commitments are a regional resource. When you buy slots, those slots are limited to a specific region or multi-region. If your only capacity commitment is in the EU then you can't create a reservation in the US. When you create a reservation, you specify a location (region) and a number of slots. Those slots are pulled from your capacity commitment in that region.

Likewise, when you run a job in a region, it only uses a reservation if the location of the job matches the location of a reservation. For example, if you assign a reservation to a project in the EU and run a query in that project on a dataset located in the US, then that query is not run on your EU reservation. In the absence of any US reservation, the job is run as on-demand.

When you choose a location for your data, consider the following:

You can interact with Cloud Storage data using BigQuery in the following ways:

• Query Cloud Storage data using BigLake or non-BigLake external tables
• Load Cloud Storage data into BigQuery
• Export data from BigQuery into Cloud Storage

When you query data in Cloud Storage by using a BigLake or a non-BigLake external table, the data you query must be colocated with your BigQuery dataset, otherwise the query incurs data transfer charges. For example:

• Single region bucket: If your BigQuery dataset is in the Warsaw (europe-central2) region, the corresponding Cloud Storage bucket must also be in the Warsaw region, or any Cloud Storage dual-region that includes Warsaw. If your BigQuery dataset is in the US multi-region, then the Cloud Storage bucket can be in the Iowa (us-central1) single region, or any dual-region that includes Iowa. Queries from any other single region incur data transfer charges, even if the bucket is in a location that is contained within the multi-region of the dataset. For example, if the external tables are in the US multi-region and the Cloud Storage bucket is in Oregon (us-west1), the job incurs data transfer charges.

  If your BigQuery dataset is in the EU multi-region, then the Cloud Storage bucket can be in the Netherlands (europe-west4) single region or any dual-region that includes Netherlands (europe-west4). Queries from any other single region incur data transfer fees, even if the bucket is in a location that is contained within the multi-region of the dataset. For example, if the external tables are in the EU multi-region and the Cloud Storage bucket is in Warsaw (europe-central2), the job incurs data transfer charges.

• Dual-region bucket: If your BigQuery dataset is in the Tokyo (asia-northeast1) region, the corresponding Cloud Storage bucket must be in the Tokyo region, or in a dual-region that includes Tokyo, like the ASIA1 dual-region.

  If the Cloud Storage bucket is in the NAM4 dual-region or any dual-region that includes the Iowa(us-central1) region, the corresponding BigQuery dataset can be in the US multi-region or in the Iowa(us-central1).

  If Cloud Storage bucket is in the EUR4 dual-region or any dual-region that includes the Netherlands (europe-west4) region, the corresponding BigQuery dataset can be in the EU multi-region or in the Netherlands (europe-west4).

• Multi-region bucket: Using multi-region dataset locations with multi-region Cloud Storage buckets is not recommended for external tables, because external query performance depends on minimal latency and optimal network bandwidth.

  If your BigQuery dataset is in the US multi-region, the corresponding Cloud Storage bucket must be in a dual-region that includes Iowa (us-central1), like the NAM4 dual-region, or in a custom dual-region that includes Iowa (us-central1).

  If your BigQuery dataset is in the EU multi-region, the corresponding Cloud Storage bucket must be in a dual-region that includes Netherlands (europe-west4), like the EUR4 dual-region, or in a custom dual-region that includes Netherlands (europe-west4) .

For more information about supported Cloud Storage locations, see Bucket locations in the Cloud Storage documentation.

When you load data from Cloud Storage, the data that you load must be colocated with your BigQuery dataset, otherwise the load job incurs data transfer charges.

For more information about load data transfer charges, see the Query Cloud Storage data section, as the same guidance applies to both batch loads and queries.

For more information, see Batch loading data.

You must consider location when querying data from Bigtable or exporting data to Bigtable.

When you query data in Bigtable through a BigQuery external table, your Bigtable instance must be in the same location as your BigQuery dataset:

• Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Bigtable instance must be in the Belgium region.
• Multi-region: Because external query performance depends on minimal latency and optimal network bandwidth, using multi-region dataset locations is not recommended for external tables on Bigtable.

For more information about supported Bigtable locations, see Bigtable locations.

• If your BigQuery dataset is in a multi-region, your Bigtable app profile must be configured to route data to a Bigtable cluster within that multi-region. For example, if your BigQuery dataset is in the US multi-region, the Bigtable cluster can be located in the us-west1 (Oregon) region, which is within the United States.
• If your BigQuery dataset is in a single region, your Bigtable app profile must be configured to route data to a Bigtable cluster in the same region. For example, if your BigQuery dataset is in the asia-northeast1 (Tokyo) region, your Bigtable cluster must also be in the asia-northeast1 (Tokyo) region.

Location considerations do not apply to Google Drive external data sources.

When you query data in Cloud SQL through a BigQuery federated query, your Cloud SQL instance must be in the same location as your BigQuery dataset.

• Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Cloud SQL instance must be in the Belgium region.
• Multi-region: If your BigQuery dataset is in the US multi-region, the corresponding Cloud SQL instance must be in a single region in the US geographic area.

For more information about supported Cloud SQL locations, see Cloud SQL locations.

When you query data in Spanner through a BigQuery federated query, your Spanner instance must be in the same location as your BigQuery dataset.

• Single region: If your BigQuery dataset is in the Belgium (europe-west1) regional location, the corresponding Spanner instance must be in the Belgium region.
• Multi-region: If your BigQuery dataset is in the US multi-region, the corresponding Spanner instance must be in a single region in the US geographic area.

For more information about supported Spanner locations, see Spanner locations.

Colocate your BigQuery dataset with your

:

• Dataproc: When you query BigQuery datasets using a BigQuery connector, your BigQuery dataset should be colocated with your Dataproc cluster.
Dataproc is supported in all Compute Engine locations.
• Vertex AI Workbench: When you query BigQuery datasets using Jupyter notebooks in Vertex AI Workbench, your BigQuery dataset should be colocated with your Vertex AI Workbench instance.
View the supported Vertex AI Workbench locations.

Develop a data management plan:

• If you choose a regional storage resource such as a BigQuery dataset or a Cloud Storage bucket, develop a plan for geographically managing your data.

You can restrict the locations in which your datasets can be created by using the Organization Policy Service. For more information, see Restricting resource locations and Resource locations supported services.

To control access to datasets in BigQuery, see Controlling access to datasets. For information about data encryption, see Encryption at rest.

• Learn how to create datasets.
• Learn about loading data into BigQuery.
• Learn about BigQuery pricing.
• View all the Google Cloud services available in locations worldwide.
• Explore additional location-based concepts, such as zones, that apply to other Google Cloud services.

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