Connect to BigQuery
With MOSTLY AI, you can connect to BigQuery and use it as a data source or destination for your synthetic data.
Prerequisites
To create a BigQuery connector, you need:
- name of the dataset for which you want to create a source or destination connector
- contents of your BigQuery key file
Get your BigQuery dataset name
For the current project, the dataset names appear in the listing on the left.

Download your BigQuery key file
- In Google Cloud BigQuery, open the main sidebar menu and select APIs & Services > Enabled APIs & services.
- From the sidebar, select Credentials.
- Click your service account.
- Select the KEYS tab.
- Click ADD KEY and select Create new key.
- In the prompt, select JSON and click Create.
Create a BigQuery connector
- From the Connectors tab, click Create connector.
The Create connector drawer appears on the right.
- On the Connect to database tab, select BigQuery.
- On the Create BigQuery connector page, configure the connector.
- For Connector name, enter a name that you can distinguish from other connectors.
A combination of
BigQuery
+_DATASET_
might help you identify this connector among other Google Cloud storage connectors. - For Connection type, select whether you want to use the connector as a source or destination.
A combination of
BigQuery
+_DATASET_
might help you identify this connector among other Google Cloud storage connectors. - For Dataset, enter the name of the dataset in BigQuery that you want to use.
- In Key file, paste the contents of your BigQuery key file.
- Click Test connection to make sure the configuration is correct.
If you see a success message, then MOSTLY AI made a successful connection to BigQuery and was able to read the dataset you specified.
- For Connector name, enter a name that you can distinguish from other connectors.
- Click Save to save your new BigQuery connector.
What's next
You can now use the BigQuery connector as a data source when you create a new catalog.
You can also use the BigQuery connector as a destination.
You can use different types of data sources and destinations for a synthetic dataset. For example, if your data source is a BigQuery database, you can deliver the generated synthetic to any of the supported databases or cloud storage providers.