Ingest data from Google Workspace

Important

This feature is in Beta. To use it, a workspace admin must turn on Lakeflow Connect for Google Workspace from the Previews page. See Manage Azure Databricks previews.

Create a managed Google Workspace ingestion pipeline in 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 Azure Databricks and create a connection. See Configure authentication to Google Workspace and Create a Google Workspace connection.

Create an ingestion pipeline

For the list of supported source tables, see Supported source tables.

Databricks UI

In the sidebar of the Azure Databricks workspace, click Data Ingestion.
On the Add data page, under Databricks connectors, click Google Workspace.
On the Connection page, select the connection that stores your Google Workspace credentials.
To create a connection, click Create connection and enter the credentials from Configure authentication to Google Workspace.
Click Next.
On the Ingestion setup page, enter a name for the pipeline.
Select a catalog and schema for the event logs.
Click Create pipeline and continue.
On the Source page, select the tables to ingest.
Click Save and continue.
On the Destination page, select a catalog and schema for the destination tables.
Click Save and continue.
(Optional) On the Schedules and notifications page, create a schedule and notifications.
Click Save and run pipeline.

Declarative Automation Bundles

Use Declarative Automation Bundles to manage Google Workspace pipelines as code. For more information, see What are Declarative Automation Bundles?.

Create a bundle using the Databricks CLI:
```
databricks bundle init
```
Add a pipeline definition file, such as resources/google_workspace_pipeline.yml. See pipeline.ingestion_definition and Examples.
Add a job definition file, such as resources/google_workspace_job.yml.
Deploy the pipeline:
```
databricks bundle deploy
```

Databricks notebook

Import the following notebook into your Azure Databricks workspace:

Get notebook
Leave cells one and two as they are. Do not modify.
Modify cell three with your pipeline configuration details. See pipeline.ingestion_definition and Examples.
Optionally configure advanced pipeline settings. See Common patterns for managed ingestion pipelines.
Click Run all.

Examples

The Google Workspace connector makes available 35 source tables in the default source schema, one for each Google Workspace application. Ingest individual tables or the tables you need.

Ingest specific tables

Declarative Automation Bundles

The following pipeline definition file ingests the gmail and login tables:

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: 'gmail'
              destination_catalog: 'main'
              destination_schema: 'google_workspace_data'
              destination_table: 'gmail'
          - table:
              source_schema: 'default'
              source_table: 'login'
              destination_catalog: 'main'
              destination_schema: 'google_workspace_data'
              destination_table: 'login'

Databricks notebook

The following pipeline specification ingests the gmail and login tables:

pipeline_name = "google_workspace_pipeline"
connection_name = "<google-workspace-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "gmail",
          "destination_catalog": "main",
          "destination_schema": "google_workspace_data",
          "destination_table": "gmail"
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "login",
          "destination_catalog": "main",
          "destination_schema": "google_workspace_data",
          "destination_table": "login"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Declarative Automation Bundles job definition file

The following example job definition runs daily.

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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Last updated on 2026-10-06