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

1. In Google Cloud BigQuery, open the main sidebar menu and select APIs & Services > Enabled APIs & services.
2. From the sidebar, select Credentials.
3. Click your service account.
4. Select the KEYS tab.
5. Click ADD KEY and select Create new key.
6. In the prompt, select JSON and click Create.

Create a BigQuery connector

1. From the Connectors tab, click Create connector. The Create connector drawer appears on the right.
2. On the Connect to database tab, select BigQuery.
3. On the Create BigQuery connector page, configure the connector.
   1. 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.

   2. 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.

   3. For Dataset, enter the name of the dataset in BigQuery that you want to use.
   4. In Key file, paste the contents of your BigQuery key file.
   5. 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.
4. 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.