This page explains the concept of data location, transfer configuration location, source data location, and how locations and transfers interact.
Locations or region types
There are 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.
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 stores your data in the selected location in accordance with the Service Specific Terms.
Transfer configurations also have locations. When you set up a transfer, if the destination dataset does not exist, you will need to create it in BigQuery before configuring the transfer. The location of the transfer configuration is automatically set to the same location that you specified for the destination dataset. The BigQuery Data Transfer Service processes and stages data in the same location as the destination BigQuery dataset.
Source data location
The source data you wish to transfer to BigQuery may also have a region. In some cases, the region where your source data is stored and the location of the destination dataset in BigQuery are irrelevant. In other kinds of transfers, the dataset and the source data must be colocated in the same region.
For transfers that require colocation, setting up a transfer to a destination dataset in a region that is different from, or not compatible with, your source data's region could result in configuration errors.
Location considerations for transfers
Colocation not necessary
The following types of transfers made by the BigQuery Data Transfer Service are not location-specific, so the location of the BigQuery dataset is irrelevant:
- Reports from Google products and services
- Transfers from external sources
Transfers from Cloud Storage to BigQuery require that the Cloud Storage bucket be colocated with the BigQuery destination dataset.Colocate your Cloud Storage buckets for transferring data.
- If your BigQuery dataset is in a multi-regional location, the Cloud Storage bucket containing the data you're transferring must be in a regional or multi-regional bucket in the same location. For example, if your BigQuery dataset is in the EU, the Cloud Storage bucket must be in a regional or multi-regional bucket in the EU.
- If your dataset is in a regional location, your Cloud Storage bucket must be a regional bucket in the same location. For example, if your dataset is in the Tokyo region, your Cloud Storage bucket must be a regional bucket in Tokyo.
- Exception: If your dataset is in the US multi-regional location, you can transfer data from a Cloud Storage bucket in any regional or multi-regional location.
Data warehouse migrations
Data warehouse migrations from Teradata require a Cloud Storage bucket as part of the transfer process. The Cloud Storage bucket must be colocated with the BigQuery destination dataset.
Redshift data warehouse migrations do not require a colocated Cloud Storage bucket.
|Region description||Region name||Notes|
|Salt Lake City||
|Multi-region description||Multi-region name|
|Data centers within member states of the European Union1||
|Data centers in the United States||
1 Data located in the
EU multi-region is not
stored in the
europe-west2 (London) or
europe-west6 (Zürich) data
- To learn more about dataset locations in BigQuery, see:
- 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.