Dataset locations

This page explains the concept of data location and the different locations where you can create datasets. To learn how to set the location for your dataset, see Creating datasets.

For information on regional pricing for BigQuery, see the Pricing page.

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

Key concepts

Locations or region types

BigQuery uses two types of 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 geographic places.

Dataset location

You specify a location for storing your BigQuery data when you create a dataset. After you create the dataset, the location cannot be changed, but you can copy the dataset to a different location, or manually move (recreate) the dataset in a different location.

BigQuery processes queries in the same location as the dataset that contains the tables you're querying.

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

Supported regions

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
Iowa	`us-central1`	Low CO₂
Las Vegas	`us-west4`
Los Angeles	`us-west2`
Montréal	`northamerica-northeast1`	Low CO₂
Northern Virginia	`us-east4`
Oregon	`us-west1`	Low CO₂
Salt Lake City	`us-west3`
São Paulo	`southamerica-east1`	Low CO₂
Santiago	`southamerica-west1`
South Carolina	`us-east1`
Toronto	`northamerica-northeast2`

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`	Low CO₂
Finland	`europe-north1`	Low CO₂
Frankfurt	`europe-west3`
London	`europe-west2`
Madrid	`europe-southwest1`	Low CO₂
Milan	`europe-west8`
Netherlands	`europe-west4`
Paris	`europe-west9`	Low CO₂
Warsaw	`europe-central2`
Zürich	`europe-west6`	Low CO₂

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 not stored in the europe-west2 (London) or europe-west6 (Zürich) data centers.

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 flat-rate 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 flat-rate reservation when submitting the job.

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

When you query data using the Google Cloud console, click More > Query settings, and for Processing Location, click Auto-select and choose your data's 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 do not match multi-region locations, even when the single-region location is associated with the multi-region location. Therefore, a job will always fail if the set of associated locations 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.

Location considerations

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

Colocate your BigQuery dataset when using external data sources.

Cloud Storage: When you query data in Cloud Storage through a BigQuery external table, the data you query must be colocated with your BigQuery dataset. For example:

Single region: If your BigQuery dataset is in the Warsaw (europe-central2) region, the corresponding Cloud Storage bucket must also be in the Warsaw region because there is currently no Cloud Storage dual-region that includes Warsaw.

EU

europe-west1

Dual-region: If your BigQuery dataset is in the Tokyo (asia-northeast1) region, the corresponding Cloud Storage bucket must be a bucket in the Tokyo region or in the ASIA1 dual-region (which includes Tokyo).
Multi-region: Because external query performance depends on minimal latency and optimal network bandwidth, using multi-region dataset locations with multi-region Cloud Storage buckets is not recommended for external tables.

supported Cloud Storage locations

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.
View the supported Bigtable locations.
Google Drive: Location considerations do not apply to Google Drive external data sources.

Colocate your BigQuery dataset with your analysis tools.

Dataproc: When you query BigQuery datasets using a BigQuery connector, your BigQuery dataset should be colocated with your Dataproc cluster.

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 notebook instance.

supported Vertex AI Workbench locations

Colocate your Cloud Storage buckets for loading data.

If your BigQuery dataset is in a multi-region, the Cloud Storage bucket containing the data you're loading 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 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.
Exception: If your dataset is in the `US` multi-region, you can load data from a Cloud Storage bucket in any location.

Colocate your Cloud Storage buckets for exporting data.

If your BigQuery dataset is in a multi-region, the Cloud Storage bucket containing the data you're exporting 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 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.
Exception: If your dataset is in the US multi-region, you can export data into a Cloud Storage bucket in any location.

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.

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

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

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.