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Create a Live Connection to Google BigQuery
Create a live connection to Google BigQuery warehouse to explore your data stored there.
Required Editions
| User Permissions Needed | |
|---|---|
| To create live connections to Google BigQuery: | Enable CRM Analytics Direct Data for external data sources |
| To explore data directly in Google BigQuery: | Explore External Data Directly |
Create the Live Connection
- In CRM Analytics, click the gear icon (
) and select Data Manager. - Click Manage Dataflows.
- In the data manager, click the Connect tab.
- Click Connect to Data.
- Click Live Connections.
Note If you don’t see the Live Connections tab, you need to enable Direct Data in CRM Analytics first. - Click Add Connection.
- Click Google BigQuery Direct Connector and enter its settings, as described in the Connection Settings section.
- When done, click Save or Save and Create Dataset.
Create Live Datasets for a Google BigQuery Source Table
After creating the live connection, create a live dataset that lets users explore Google BigQuery tables or views available with the connection.
After creating the live connection, enable a live dataset for the connection.
- In the data manager, click the Data tab.
- On the Live Datasets tab, click Create Dataset. Select the Google BigQuery connection and enable a Google BigQuery source table that is available with the connection.
- Name the live dataset and assign it to a CRM Analytics app. Use the app to control which Salesforce users, roles, and groups have access to explore Google BigQuery data with the connection.
- When done, click Create Live Dataset.
To begin exploring, click the live dataset from the Datasets tab in CRM Analytics Studio.
Google BigQuery Live Connections Settings
All settings require a value, unless otherwise indicated. The service account that you use to connect to Google Big Query must have the standard role "BigQuery Data Viewer." Account creation generates a JSON file of account properties. Most of the necessary connection settings are in this file.
| Setting | Description |
|---|---|
| Connection Name | Identifies the live connection. Use a convention that lets you easily distinguish between different connections. |
| Developer Name | API name for the connection. This name must be unique, begin with a letter, and can only contain underscores and alphanumeric characters. This name can’t include spaces, end with an underscore, or contain two consecutive underscores. You can’t change the developer name after you create the connection. |
| Description | Description of the connection for internal use. |
| Client Email | Enter the client_email value from the JSON
file. |
| Private Key | Enter the Don’t include |
| Project ID | Enter the If you have multiple projects with the same service account, enter the ID of the project containing the live dataset that you want to create a connection to. |
| Storage Path | Optional setting. Path in Google Cloud Storage where a local stage file is created to store the data temporarily. Enter the bucket name or the bucket name and folder name. For example, enter <bucket_name> or <bucket_name>/<folder_name>. To write files to this staging area, the user specified in Client Email must have the list, read, and write permissions on the bucket. |
| Schema | Google BigQuery schema whose data you want to explore with the connection. You can either add all the tables you want to explore to a single Google BigQuery schema or create a separate connection for each schema. To keep data secure, we recommend creating a separate Google BigQuery schema specifically for Direct Data for Google BigQuery. |
| Region ID | Optional setting. Enter the data location of the live Google BigQuery dataset if the location isn’t in North America. |
| Use Storage API | Optional setting. If True, uses the Google BigQuery Storage Read API. See Google’s BigQuery Storage Read API. If False, uses Direct extract mode. |
