BigQuery locations

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.

Locations and regions

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 Reliability: Disaster planning.

Supported locations

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

Regions

The following table lists the regions in the Americas where BigQuery is available.
Region description Region name Details
Columbus, Ohio us-east5
Dallas us-south1
Iowa us-central1 leaf icon Low CO2
Las Vegas us-west4
Los Angeles us-west2
Montréal northamerica-northeast1 leaf icon Low CO2
Northern Virginia us-east4
Oregon us-west1 leaf icon Low CO2
Salt Lake City us-west3
São Paulo southamerica-east1 leaf icon Low CO2
Santiago southamerica-west1 leaf icon Low CO2
South Carolina us-east1
Toronto northamerica-northeast2 leaf icon Low CO2
The following table lists the regions in Asia Pacific where BigQuery is available.
Region description Region name Details
Delhi asia-south2
Hong Kong asia-east2
Jakarta asia-southeast2
Melbourne australia-southeast2
Mumbai asia-south1
Osaka asia-northeast2
Seoul asia-northeast3
Singapore asia-southeast1
Sydney australia-southeast1
Taiwan asia-east1
Tokyo asia-northeast1
The following table lists the regions in Europe where BigQuery is available.
Region description Region name Details
Belgium europe-west1 leaf icon Low CO2
Finland europe-north1 leaf icon Low CO2
Frankfurt europe-west3 leaf icon Low CO2
London europe-west2 leaf icon Low CO2
Madrid europe-southwest1
Milan europe-west8
Netherlands europe-west4
Paris europe-west9 leaf icon Low CO2
Turin europe-west12
Warsaw europe-central2
Zürich europe-west6 leaf icon Low CO2
The following table lists the regions in the Middle East where BigQuery is available.
Region description Region name Details
Dammam me-central2
Doha me-central1
Tel Aviv me-west1

Multi-regions

The following table lists the multi-regions where BigQuery is available.
Multi-region description Multi-region name
Data centers within member states of the European Union1 EU
Data centers in the United States US

1 Data located in the EU multi-region is only stored in the europe-west1 (Belgium) or europe-west4 (Netherlands) data centers.

BigQuery Omni locations

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 Introduction to BigQuery editions.
Region description Region name Collocated BigQuery region
AWS
AWS - US East (N. Virginia) aws-us-east-1 us-east4
AWS - US West (Oregon) aws-us-west-2 us-west1
AWS - Asia Pacific (Seoul) aws-ap-northeast-2 asia-northeast3
AWS - Europe (Ireland) aws-eu-west-1 europe-west1
Azure
Azure - East US 2 azure-eastus2 us-east4

BigQuery ML locations

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.

BigQuery ML model prediction and other ML functions are supported in all BigQuery regions. Support for model training varies by region:

  • Training for built-in models (linear regression, logistic regression, kmeans, matrix factorization, and time series) and imported models is supported in all BigQuery regions.

  • Training for DNN and boosted trees using XGBoost models is available in the multi-regions US and EU, and most single regions. See the following table for more information.

  • Training for AutoML is supported in the US and EU multi-regions and in most single regions.

Locations for non-remote models

Regional locations
Region description Region name Imported
models		 Built-in
model
training		 DNN/Autoencoder/
Boosted Tree/
Wide-and-Deep models
training		 AutoML
model
training		 Hyperparameter
tuning		 Vertex AI Model Registry integration
Americas
Columbus, Ohio us-east5 ● ●
Dallas us-south1 ● ●
Iowa us-central1 ● ● ● ● ● ●
Las Vegas us-west4 ● ● ● ●
Los Angeles us-west2 ● ● ● ●
Montréal northamerica-northeast1 ● ● ● ● ● ●
Northern Virginia us-east4 ● ● ● ● ● ●
Oregon us-west1 ● ● ● ● ●
Salt Lake City us-west3 ● ● ●
São Paulo southamerica-east1 ● ● ● ●
Santiago southamerica-west1 ● ●
South Carolina us-east1 ● ● ● ● ●
Toronto northamerica-northeast2 ● ● ●
Europe
Belgium europe-west1 ● ● ● ● ● ●
Finland europe-north1 ● ● ●
Frankfurt europe-west3 ● ● ● ● ● ●
London europe-west2 ● ● ● ● ● ●
Madrid europe-southwest1 ● ●
Milan europe-west8 ● ●
Netherlands europe-west4 ● ● ● ● ● ●
Paris europe-west9 ● ●
Turin europe-west12 ●
Warsaw europe-central2 ● ●
Zürich europe-west6 ● ● ● ● ● ●
Asia Pacific
Delhi asia-south2 ● ●
Hong Kong asia-east2 ● ● ● ● ● ●
Jakarta asia-southeast2 ● ● ●
Melbourne australia-southeast2 ● ●
Mumbai asia-south1 ● ● ● ● ●
Osaka asia-northeast2 ● ● ●
Seoul asia-northeast3 ● ● ● ● ● ●
Singapore asia-southeast1 ● ● ● ● ● ●
Sydney australia-southeast1 ● ● ● ● ● ●
Taiwan asia-east1 ● ● ● ● ● ●
Tokyo asia-northeast1 ● ● ● ● ● ●
Middle East
Dammam me-central2 ●
Doha me-central1 ●
Tel Aviv me-west1 ● ●

Multi-regional locations

Region description Region name Imported
models		 Built-in
model
training		 DNN/Autoencoder/
Boosted Tree/
Wide-and-Deep models training		 AutoML
model
training		 Hyperparameter
tuning		 Vertex AI Model Registry integration
Data centers within member states of the European Union1 EU ● ● ● ● ● ●
Data centers in the United States US ● ● ● ● ● ●

1 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.

Locations for remote models

Regional locations
The following table shows which regions are supported for different types of [remote models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model). The column name indicates the type remote model.
Region description Region name Vertex AI deployed models Text generation LLMs Text embedding LLMs Cloud Natural Language API Cloud Translation API Cloud Vision API Document AI API Speech-to-Text API
Americas
Columbus, Ohio us-east5
Dallas us-south1 ●
Iowa us-central1 ● ● ●
Las Vegas us-west4 ● ●
Los Angeles us-west2 ●
Montréal northamerica-northeast1 ●
Northern Virginia us-east4 ●
Oregon us-west1 ●
Salt Lake City us-west3 ●
São Paulo southamerica-east1 ●
Santiago southamerica-west1
South Carolina us-east1 ●
Toronto northamerica-northeast2 ●
Europe
Belgium europe-west1 ● ●
Finland europe-north1
Frankfurt europe-west3 ● ●
London europe-west2 ● ●
Madrid europe-southwest1
Milan europe-west8
Netherlands europe-west4 ● ●
Paris europe-west9 ● ●
Turin europe-west12
Warsaw europe-central2 ●
Zürich europe-west6 ●
Asia Pacific
Delhi asia-south2
Hong Kong asia-east2 ●
Jakarta asia-southeast2 ●
Melbourne australia-southeast2
Mumbai asia-south1 ●
Osaka asia-northeast2
Seoul asia-northeast3 ● ●
Singapore asia-southeast1 ● ●
Sydney australia-southeast1 ●
Taiwan asia-east1 ●
Tokyo asia-northeast1 ●
Middle East
Dammam me-central2
Doha me-central1
Tel Aviv me-west1 ●

Multi-regional locations

The following table shows which multi-regions are supported for different types of [remote models](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create-remote-model). The column name indicates the type remote model.
Region description Region name Vertex AI deployed models Text generation LLMs Text embedding LLMs Cloud Natural Language API Cloud Translation API Cloud Vision API Document AI API Speech-to-Text API
Data centers within member states of the European Union1 EU ● ● ● ● ● ●
Data centers in the United States US ● ● ● ● ● ● ●

Specify locations

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.

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 More > Query settings, expand Advanced options, and then select your Data location.
  • 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.

Locations, reservations, and jobs

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.

Location considerations

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

Cloud Storage

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

Query Cloud Storage data

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. 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 Cloud Storage bucket can be in the US multi-region, the Iowa (us-central1) single region, or any dual-region that includes Iowa. Queries from any other single region fails, 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 fails.

    If your BigQuery dataset is in the EU multi-region, then Cloud Storage bucket can be in the EU multi-region, the Belgium (europe-west1) single region, or any dual-region that includes Belgium. Queries from any other single region fails, 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 fails.

  • 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. For more information, see Create a dual-region bucket.

    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 Belgium(europe-west1) region, the corresponding BigQuery dataset can be in the EU multi-region or in the Belgium(europe-west1).

  • 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 the US multi-region, 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 the EU multi-region, in a dual-region that includes Belgium (europe-west1), like the EUR4 dual-region, or in a custom dual-region that includes Belgium.

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

Load data from Cloud Storage

When you load data from Cloud Storage by using a BigLake or a non-BigLake external table, the data you load must be colocated with your BigQuery dataset.

  • You can load data from a Cloud Storage bucket located in any location if your BigQuery dataset is located in the US multi-region."

  • Multi-region bucket: If the Cloud Storage bucket that you want to load from is located in a multi-region bucket, then your BigQuery dataset can be in the same multi-region bucket or any single region that is included in the same multi-region bucket. For example, if the Cloud Storage bucket is in the EU region, then your BigQuery dataset can be in the EU multi-region or any single region in the EU.

  • Dual-region bucket: If the Cloud Storage bucket that you want to load from is located in a dual-region bucket, then your BigQuery dataset can be located in regions that are included in the dual-region bucket, or in a multi-region that includes the dual-region. For example, if your Cloud Storage bucket is located in the EUR4 region, then your BigQuery dataset can be located in either the Finland (europe-north1) single-region, the Netherlands (europe-west4) single-region, or the EU multi-region.

    For more information, see Create a dual-region bucket.

  • Single region bucket: If your Cloud Storage bucket that you want to load from is in a single-region, your BigQuery dataset can be in the same single-region, or in the multi-region that includes the single-region. For example, if you Cloud Storage bucket is in the Finland (europe-north1) region, your BigQuery dataset can be in the Finland or the EU multi-region.

  • One exception is that if your BigQuery dataset is located in the asia-northeast1 region, then your Cloud Storage bucket can be located in the EU multi-region.

For more information, see Batch loading data.

Export data into Cloud Storage

Colocate your Cloud Storage buckets for exporting data:
  • If your BigQuery dataset is in the EU multi-region, the Cloud Storage bucket containing the data that you export must be in the same multi-region or in a location that is contained within the multi-region. For example, if your BigQuery dataset is in the EU multi-region, the Cloud Storage bucket can be located in the europe-west1 Belgium region, which is within the EU.

    If your dataset is in the US multi-region, you can export data into a Cloud Storage bucket in any location.

  • If your dataset is in a region, your Cloud Storage bucket must be in the same region. For example, if your dataset is in the asia-northeast1 Tokyo region, your Cloud Storage bucket cannot be in the ASIA multi-region.

For more information, see Exporting table data.

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.

Google Drive

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

Cloud SQL

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.

Spanner

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.

Analysis tools

Colocate your BigQuery dataset with your analysis tools:

Data management plans

Develop a data management plan:

Restrict locations

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.

Dataset security

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

Next steps