Connect to Google Cloud object storage
To use datasets that you keep in Google Cloud storage buckets as a data source for synthetic data, you need to create a Google Cloud connector in MOSTLY AI.
If you want to keep the generated synthetic data in a separate bucket, you need another Google Cloud connector that points to the bucket.
To create a Google Cloud bucket connector, you need the contents of your Google Cloud key file and the name of the bucket where you keep your original data.
- 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.
- From the Connectors tab, click Create connector. The Create connector drawer appears on the right.
- On the Connect to cloud storage tab, select Google cloud storage.
- On the Create Google cloud connector page, configure the connector.
For Connector name, enter a name that you can distinguish from other connectors.💡
A combination of
_BUCKET_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.
You can select only data source connectors when you create a new catalog.
Similarly, you can select only data destination connectors when you configure a destination for the new synthetic dataset.
For Key file, paste the contents of your Google Cloud Storage key file.
For Bucket, enter the name of the Google Cloud Storage bucket.
- Click Test connection to make sure the configuration is correct. If you see a successful connection message, then MOSTLY AI connected to Google Cloud object storage successfully and found the provided bucket.
- Click Save to save your new Google Cloud bucket connector.
You can now use the Google Cloud storage connector as a data source when you create a new catalog.
You can also use the Google Cloud storage 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 Google Cloud storage database, you can deliver the generated synthetic to any of the supported databases or cloud storage providers.