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
|
Low CO
2
|
Las Vegas
|
us-west4
|
|
Los Angeles
|
us-west2
|
|
Montréal
|
northamerica-northeast1
|
Low CO
2
|
Northern Virginia
|
us-east4
|
|
Oregon
|
us-west1
|
Low CO
2
|
Salt Lake City
|
us-west3
|
|
São Paulo
|
southamerica-east1
|
Low CO
2
|
Santiago
|
southamerica-west1
|
Low CO
2
|
South Carolina
|
us-east1
|
|
Toronto
|
northamerica-northeast2
|
Low CO
2
|
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 CO
2
|
Berlin
|
europe-west10
|
|
Finland
|
europe-north1
|
Low CO
2
|
Frankfurt
|
europe-west3
|
Low CO
2
|
London
|
europe-west2
|
Low CO
2
|
Madrid
|
europe-southwest1
|
|
Milan
|
europe-west8
|
|
Netherlands
|
europe-west4
|
|
Paris
|
europe-west9
|
Low CO
2
|
Turin
|
europe-west12
|
|
Warsaw
|
europe-central2
|
|
Zürich
|
europe-west6
|
Low CO
2
|
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
|
|
The following table lists the regions in Africa where BigQuery is available.
Region description
|
Region name
|
Details
|
Johannesburg
|
africa-south1
|
|
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 Union
1
|
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 Studio locations
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:
|
Region description
|
Region name
|
Details
|
Africa
|
|
Johannesburg
|
africa-south1
|
|
Americas
|
|
Columbus
|
us-east5
|
|
|
Dallas
|
us-south1
|
|
|
Iowa
|
us-central1
|
Low CO
2
|
|
Los Angeles
|
us-west2
|
|
|
Las Vegas
|
us-west4
|
|
|
Montreal
|
northamerica-northeast1
|
Low CO
2
|
|
N. Virginia
|
us-east4
|
|
|
Oregon
|
us-west1
|
Low CO
2
|
|
São Paulo
|
southamerica-east1
|
Low CO
2
|
|
South Carolina
|
us-east1
|
|
Asia Pacific
|
|
Hong Kong
|
asia-east2
|
|
|
Jakarta
|
asia-southeast2
|
|
|
Mumbai
|
asia-south1
|
|
|
Seoul
|
asia-northeast3
|
|
|
Singapore
|
asia-southeast1
|
|
|
Sydney
|
australia-southeast1
|
|
|
Taiwan
|
asia-east1
|
|
|
Tokyo
|
asia-northeast1
|
|
Europe
|
|
Belgium
|
europe-west1
|
Low CO
2
|
|
Frankfurt
|
europe-west3
|
Low CO
2
|
|
London
|
europe-west2
|
Low CO
2
|
|
Madrid
|
europe-southwest1
|
|
|
Netherlands
|
europe-west4
|
|
|
Turin
|
europe-west12
|
|
|
Zürich
|
europe-west6
|
Low CO
2
|
Middle East
|
|
Doha
|
me-central1
|
|
|
Dammam
|
me-central2
|
|
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
|
Colocated 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 - Asia Pacific (Sydney)
|
aws-ap-southeast-2
|
australia-southeast1
|
|
AWS - Europe (Ireland)
|
aws-eu-west-1
|
europe-west1
|
|
AWS - Europe (Frankfurt)
|
aws-eu-central-1
|
europe-west3
|
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
internally trained models
and
imported models
is supported in all BigQuery regions.
Training for autoencoder, boosted tree, DNN, and wide-and-deep 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
|
●
|
●
|
●
|
●
|
●
|
●
|
|
Berlin
|
europe-west10
|
●
|
●
|
|
|
|
|
|
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
|
●
|
●
|
|
|
|
|
Africa
|
|
Johannesburg
|
africa-south1
|
●
|
●
|
|
|
|
|
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 Union
1
|
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
.
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
.
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 Union
1
|
EU
|
|
●
|
|
●
|
●
|
●
|
●
|
●
|
Data centers in the United States
|
US
|
|
●
|
●
|
●
|
●
|
●
|
●
|
●
|
BigQuery SQL translator locations
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:
|
Region description
|
Region name
|
Details
|
Asia Pacific
|
|
Tokyo
|
asia-northeast1
|
|
|
Mumbai
|
asia-south1
|
|
|
Singapore
|
asia-southeast1
|
|
|
Sydney
|
australia-southeast1
|
|
Europe
|
|
EU multi-region
|
eu
|
|
Warsaw
|
europe-central2
|
|
|
Finland
|
europe-north1
|
Low CO
2
|
|
Madrid
|
europe-southwest1
|
|
|
Belgium
|
europe-west1
|
Low CO
2
|
|
London
|
europe-west2
|
Low CO
2
|
|
Frankfurt
|
europe-west3
|
Low CO
2
|
|
Netherlands
|
europe-west4
|
|
|
Zürich
|
europe-west6
|
Low CO
2
|
|
Paris
|
europe-west9
|
Low CO
2
|
|
Turin
|
europe-west12
|
|
Americas
|
|
São Paulo
|
southamerica-east1
|
Low CO
2
|
|
US multi-region
|
us
|
|
Iowa
|
us-central1
|
Low CO
2
|
|
South Carolina
|
us-east1
|
|
|
Northern Virginia
|
us-east4
|
|
|
Columbus, Ohio
|
us-east5
|
|
|
Dallas
|
us-south1
|
|
|
Oregon
|
us-west1
|
Low CO
2
|
|
Los Angeles
|
us-west2
|
|
|
Salt Lake City
|
us-west3
|
|
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. 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 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:
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
.
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
.
What's next