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            Google BigQuery Standard SQL Connection

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            Google BigQuery Standard SQL Connection

            Bring your large volumes of Google BigQuery data with standard SQL support into Salesforce Data Pipelines using the Google BigQuery Standard SQL connector.

            Important
            Important Use the Google BigQuery for Legacy SQL Connection instead of this connector if using legacy SQL.

            Create Connection

            1. On the Data Manager Connections tab, click New Connection.
            2. Click the name of the connector, and click Next.
            3. Enter the connector settings.
            4. To validate your settings and attempt to connect to the source, click Save & Test. If the connection fails, Salesforce Data Pipelines shows possible reasons.

            All settings require a value, unless otherwise indicated.

            Connection Setting Description
            Connection Name Identifies the connections. Use a convention that lets you easily distinguish between different connections.
            Developer Name API name for the connection. This name can’t include spaces. The API name is used in your recipes to reference data extracted through this connection. You can’t change the developer name after you create the connection.
            Description Description
            Schema Name of the Google BigQuery Dataset ID you’re connecting to.
            Private Key Enter the private_key value from the JSON file.
            Client Email

            Enter the client_email value from the JSON file.

            Don’t include "private_key": "-----BEGIN PRIVATE KEY-----" at the start of the key, and \n-----END PRIVATE KEY-----\n" at the end.
            Project ID

            Enter the project_id value from the JSON file.

            If you have multiple projects with the same service account, enter the ID of the project containing the dataset that you want to connect to.
            Billing Project ID The ID for the Cloud Billing account you want this connection’s activity charged to.
            Use Storage API

            Optional setting.

            True: Uses the Google BigQuery Storage Read API. See Google’s BigQuery Storage Read API documentation to understand your expected consumption, and Google’s data extraction pricing for additional cost information.

            False: Uses the Direct extract mode. This mode has a data limit of 10MB.

            Google BigQuery Standard SQL Connector Considerations

            Keep these behaviors in mind when working with the Google BigQuery Standard SQL connector.

            • Connected object names must start with a letter and contain only letters, digits, or underscores. Object names can’t end with an underscore.
            • Only field names with combinations of alphanumeric, dot, underscore, or dash characters are supported. If a connector includes field names that contain other characters, such as spaces or brackets, the sync fails.
            • The connector can sync up to 100 million rows or 50 GB per object, whichever limit is reached first.
            • Staging mode is supported. Direct mode isn’t supported.
            • BigQuery tables using data integrated from Google Drive aren’t supported. The Google Drive data must be moved into BigQuery.
            • The connector doesn’t flatten nested fields. When previewing nested data before load, only the top-level fields are shown.
            Temporary Destination Tables
            The connector creates temporary destination tables in BigQuery to stage data before serving it to the client. The connector names these tables in the format EA_TEMP_<5-character random string>_<objectName>. Tables are usually deleted automatically. To avoid incurring extra data storage costs on Google BigQuery, check that all temporary tables have been deleted, and manually delete any tables that haven’t been deleted.
            Table Support
            The Google BigQuery Standard SQL connector supports Standard SQL Tables, Legacy SQL Tables, and Standard SQL Views, but doesn’t support Legacy SQL Views.
             
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