Ingest data from Google Workspace

Important

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

This page shows how to create a managed Google Drive Audit Logs 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 Azure Databricks and create a connection. See Configure authentication to Google Workspace and Create a Google Drive Audit Logs connection.

Create an ingestion pipeline

The Google Drive Audit Logs connector ingests a single source table, drive, under the default source schema. For details, 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 Drive Audit Logs.
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 Create connection to create a connection with 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 a schema to write event logs to. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, click Create schema in the drop-down menu to create a schema.
Click Create pipeline and continue.
On the Source page, select the drive table to ingest.
Click Save and continue.
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 Create schema in the drop-down menu to create a schema.
Click Save and continue.
(Optional) On the Schedules and notifications page, click Create schedule. Set the frequency to refresh the destination tables.
(Optional) Click Add notification to set email notifications for pipeline operation success or failure, then click Save and run pipeline.

Declarative Automation Bundles

Use Declarative Automation Bundles to manage Google Drive Audit Logs pipelines as code. Bundles can contain YAML definitions of jobs and tasks, are managed using the Databricks CLI, and can be shared and run in different target workspaces (such as development, staging, and production). For more information, see What are Declarative Automation Bundles?.

Create a bundle using the Databricks CLI:
```
databricks bundle init
```
Add two new resource files to the bundle:
- A pipeline definition file (for example, resources/google_drive_audit_logs_pipeline.yml). See pipeline.ingestion_definition and Examples.
- A job definition file that controls the frequency of data ingestion (for example, resources/google_drive_audit_logs_job.yml).
Deploy the pipeline using the Databricks CLI:
```
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 Drive Audit Logs connector makes available the drive table in the default source schema. For details, see Supported source tables.

Ingest the Drive audit table

Declarative Automation Bundles

The following pipeline definition file ingests the Google Drive Audit Logs drive table:

resources:
  pipelines:
    google_drive_audit_logs_pipeline:
      name: google_drive_audit_logs_pipeline
      catalog: 'main'
      target: 'google_drive_audit_logs_data'
      ingestion_definition:
        connection_name: google_drive_audit_logs_connection
        objects:
          - table:
              source_schema: 'default'
              source_table: 'drive'
              destination_catalog: 'main'
              destination_schema: 'google_drive_audit_logs_data'
              destination_table: 'drive'

Databricks notebook

The following pipeline specification ingests the Google Drive Audit Logs drive table:

pipeline_name = "google_drive_audit_logs_pipeline"
connection_name = "<google-drive-audit-logs-connection>"
pipeline_spec = {
  "name": pipeline_name,
  "ingestion_definition": {
    "connection_name": connection_name,
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "drive",
          "destination_catalog": "main",
          "destination_schema": "google_drive_audit_logs_data",
          "destination_table": "drive"
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Declarative Automation Bundles job definition file

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

Declarative Automation Bundles

resources:
  jobs:
    google_drive_audit_logs_job:
      name: google_drive_audit_logs_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: google_drive_audit_logs_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.google_drive_audit_logs_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 2026-08-13