This page explains the concept of data location and the different locations where you can create BigQuery datasets and BigQuery ML models.
For information on regional pricing for BigQuery ML, see the Pricing page.
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 to store your BigQuery ML models and training data. 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 ML processes and stages data in the same location as the target dataset.
BigQuery ML stores your data in the selected location in accordance with the Service Specific Terms.
BigQuery ML supports the following locations.
|Region description||Region name|
|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
- Read an overview of BigQuery ML
- To get started using BigQuery ML, see Getting started with BigQuery ML using the web UI.
- 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.