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Ingest data from Google Workspace

Beta

This feature is in Beta. Workspace admins can control access to this feature from the Previews page. See Manage Databricks previews.

This page shows how to create a managed Google Workspace ingestion pipeline using Lakeflow Connect.

Requirements​

  • To create an ingestion pipeline, first meet the following requirements:

    • Your workspace must be enabled for Unity Catalog.

    • Serverless compute must be enabled for your workspace. See Serverless compute requirements.

    • To create a new connection, you must have CREATE CONNECTION privileges on the metastore. See Manage privileges in Unity Catalog.

      If the connector supports UI-based pipeline authoring, an admin can create the connection and the pipeline at the same time by completing the steps on this page. However, if the users who create pipelines use API-based pipeline authoring or are non-admin users, an admin must first create the connection in Catalog Explorer. See Connect to managed ingestion sources.

    • To use an existing connection, you must have USE CONNECTION privileges or ALL PRIVILEGES on the connection object.

    • You must have USE CATALOG privileges on the target catalog.

    • You must have USE SCHEMA and CREATE TABLE privileges on an existing schema or CREATE SCHEMA privileges on the target catalog.

  • To ingest from Google Workspace, first configure authentication from Databricks and create a connection. See Configure authentication to Google Workspace and Create a Google Workspace connection.

Create an ingestion pipeline​

The Google Workspace connector ingests 33 source tables, one for each Google Workspace activity application, under the default source schema. For details, see Supported source tables.

  1. In the sidebar of the Databricks workspace, click Data Ingestion.
  2. On the Add data page, under Databricks connectors, click Google Workspace.
  3. On the Connection page of the ingestion wizard, select the connection that stores your Google Workspace credentials. If you have the CREATE CONNECTION privilege on the metastore, click Plus icon. Create connection to create a connection with the credentials from Configure authentication to Google Workspace.
  4. Click Next.
  5. On the Ingestion setup page, enter a name for the pipeline.
  6. Select a catalog and a schema to write event logs to. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, click Plus icon. Create schema in the drop-down menu to create a schema.
  7. Click Create pipeline and continue.
  8. On the Source page, select one or more activity application tables to ingest (e.g., calendar, chat, admin). The UI lists 30 applications; to ingest login, groups, or groups_enterprise, use the Declarative Automation Bundles or Databricks notebook option instead.
  9. Click Save and continue.
  10. On the Destination page, select a catalog and a schema to load data into. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, click Plus icon. Create schema in the drop-down menu to create a schema.
  11. Click Save and continue.
  12. (Optional) On the Schedules and notifications page, click Plus icon. Create schedule. Set the frequency to refresh the destination tables.
  13. (Optional) Click Plus icon. Add notification to set email notifications for pipeline operation success or failure, then click Save and run pipeline.

Examples​

The Google Workspace connector makes available 33 tables in the default source schema, one for each activity application. For details, see Supported source tables.

Ingest activity application tables​

The following pipeline definition file ingests Google Workspace activity tables:

YAML
resources:
  pipelines:
    google_workspace_pipeline:
      name: google_workspace_pipeline
      catalog: 'main'
      target: 'google_workspace_data'
      ingestion_definition:
        connection_name: google_workspace_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'calendar'
              destination_catalog: 'main'
              destination_schema: 'google_workspace_data'
              destination_table: 'calendar'
          - table:
              source_schema: 'default'
              source_table: 'chat'
              destination_catalog: 'main'
              destination_schema: 'google_workspace_data'
              destination_table: 'chat'
          - table:
              source_schema: 'default'
              source_table: 'admin'
              destination_catalog: 'main'
              destination_schema: 'google_workspace_data'
              destination_table: 'admin'

Declarative Automation Bundles job definition file​

The following is an example job definition file for use with Declarative Automation Bundles. The job runs daily.

YAML
resources:
  jobs:
    google_workspace_job:
      name: google_workspace_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: google_workspace_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.google_workspace_pipeline.id}

Common patterns​

For advanced pipeline configurations, see Common patterns for managed ingestion pipelines.

Next steps​

Start, schedule, and set alerts on your pipeline. See Common pipeline maintenance tasks.

Additional resources​

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