Ingest aggregated data from Google Analytics

Important

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

Create a managed Lakeflow Connect pipeline that ingests aggregated Google Analytics 4 (GA4) report data into Unity Catalog tables. Use Declarative Automation Bundles or a Azure Databricks notebook.

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 Analytics, first configure authentication from Azure Databricks and create a connection. See Configure authentication to Google Analytics and Create a Google Analytics Aggregated connection.

Connector options

For the options the Google Analytics Aggregated connector accepts, their scope, and the tables they apply to, see Connector options. Set pipeline-scoped options in source_configurations and table-scoped options on the individual object. See Examples for usage.

Create an ingestion pipeline

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

Declarative Automation Bundles

Use Declarative Automation Bundles to manage Google Analytics Aggregated 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). See What are Declarative Automation Bundles?.

  1. Create a bundle using the Databricks CLI:

    databricks bundle init

  2. Add two new resource files to the bundle:

    • A pipeline definition file (for example, resources/google_analytics_aggregated_pipeline.yml). See pipeline.ingestion_definition and Examples.
    • A job definition file that controls the frequency of data ingestion (for example, resources/google_analytics_aggregated_job.yml).

  3. Deploy the pipeline using the Databricks CLI:

    databricks bundle deploy

Databricks notebook

  1. Import the following notebook into your Azure Databricks workspace:

    Get notebook

  2. Leave cells one, two, and three as they are.

  3. Modify cell four with your pipeline configuration details. See pipeline.ingestion_definition and Examples.

  4. Optionally configure advanced pipeline settings. See Common patterns for managed ingestion pipelines.

  5. Click Run all.

Examples

The Google Analytics Aggregated connector makes its source tables available in the default source schema. For the list of tables, see Supported source tables.

For schema details, see Destination table schemas.

Ingest a prebuilt report table

The following example ingests the traffic_acquisition_session_source_medium_report table for two GA4 properties.

Declarative Automation Bundles

resources:
  pipelines:
    google_analytics_aggregated_pipeline:
      name: google_analytics_aggregated_pipeline
      catalog: 'main'
      target: 'google_analytics_aggregated_data'
      ingestion_definition:
        connection_name: google_analytics_aggregated_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_date: '<start-date>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'traffic_acquisition_session_source_medium_report'
              destination_catalog: 'main'
              destination_schema: 'google_analytics_aggregated_data'
              destination_table: 'traffic_acquisition_session_source_medium_report'
              connector_options:
                api_source_connector_options:
                  options:
                    property_ids: '["<property-id-1>", "<property-id-2>"]'
                    lookback_window_in_days: '<lookback-days>'

Databricks notebook

pipeline_spec = {
  "name": "<pipeline-name>",
  "catalog": "<catalog-name-for-event-logs>",
  "schema": "<schema-name-for-event-logs>",
  "ingestion_definition": {
    "connection_name": "<connection-name>",
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_date": "<start-date>"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "traffic_acquisition_session_source_medium_report",
          "destination_catalog": "<destination-catalog>",
          "destination_schema": "<destination-schema>",
          "destination_table": "traffic_acquisition_session_source_medium_report",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "property_ids": "[\"<property-id-1>\", \"<property-id-2>\"]",
                "lookback_window_in_days": "<lookback-days>"
              }
            }
          }
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest a custom report

The following example ingests a custom report that aggregates sessions and users by country and device category for each GA4 week. To ingest more than one custom report in the same pipeline, add a custom_report object for each report, each with a different destination_table. For the options, see Custom reports.

Declarative Automation Bundles

resources:
  pipelines:
    google_analytics_aggregated_pipeline:
      name: google_analytics_aggregated_pipeline
      catalog: 'main'
      target: 'google_analytics_aggregated_data'
      ingestion_definition:
        connection_name: google_analytics_aggregated_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_date: '<start-date>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'custom_report'
              destination_catalog: 'main'
              destination_schema: 'google_analytics_aggregated_data'
              destination_table: 'weekly_sessions_by_country_and_device'
              connector_options:
                api_source_connector_options:
                  options:
                    property_id: '<property-id>'
                    dimensions: '["country", "deviceCategory"]'
                    metrics: '["sessions", "totalUsers"]'
                    cursor_dimension: 'yearWeek'

Databricks notebook

pipeline_spec = {
  "name": "<pipeline-name>",
  "catalog": "<catalog-name-for-event-logs>",
  "schema": "<schema-name-for-event-logs>",
  "ingestion_definition": {
    "connection_name": "<connection-name>",
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_date": "<start-date>"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "custom_report",
          "destination_catalog": "<destination-catalog>",
          "destination_schema": "<destination-schema>",
          "destination_table": "weekly_sessions_by_country_and_device",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "property_id": "<property-id>",
                "dimensions": "[\"country\", \"deviceCategory\"]",
                "metrics": "[\"sessions\", \"totalUsers\"]",
                "cursor_dimension": "yearWeek"
              }
            }
          }
        }
      }
    ]
  }
}
json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Ingest prebuilt and custom report tables together

The following example combines a prebuilt report table and a monthly custom report in a single pipeline. Each object sets only the connector options that its table accepts.

Declarative Automation Bundles

resources:
  pipelines:
    google_analytics_aggregated_pipeline:
      name: google_analytics_aggregated_pipeline
      catalog: 'main'
      target: 'google_analytics_aggregated_data'
      ingestion_definition:
        connection_name: google_analytics_aggregated_connection
        source_configurations:
          - api_source_connector_config:
              configs:
                start_date: '<start-date>'
        objects:
          - table:
              source_schema: 'default'
              source_table: 'website_overview'
              destination_catalog: 'main'
              destination_schema: 'google_analytics_aggregated_data'
              destination_table: 'website_overview'
              connector_options:
                api_source_connector_options:
                  options:
                    property_ids: '["<property-id>"]'
          - table:
              source_schema: 'default'
              source_table: 'custom_report'
              destination_catalog: 'main'
              destination_schema: 'google_analytics_aggregated_data'
              destination_table: 'monthly_revenue_by_channel'
              connector_options:
                api_source_connector_options:
                  options:
                    property_id: '<property-id>'
                    dimensions: '["sessionDefaultChannelGroup"]'
                    metrics: '["totalRevenue", "transactions"]'
                    cursor_dimension: 'yearMonth'

Databricks notebook

pipeline_spec = {
  "name": "<pipeline-name>",
  "catalog": "<catalog-name-for-event-logs>",
  "schema": "<schema-name-for-event-logs>",
  "ingestion_definition": {
    "connection_name": "<connection-name>",
    "source_configurations": [
      {
        "api_source_connector_config": {
          "configs": {
            "start_date": "<start-date>"
          }
        }
      }
    ],
    "objects": [
      {
        "table": {
          "source_schema": "default",
          "source_table": "website_overview",
          "destination_catalog": "<destination-catalog>",
          "destination_schema": "<destination-schema>",
          "destination_table": "website_overview",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "property_ids": "[\"<property-id>\"]"
              }
            }
          }
        }
      },
      {
        "table": {
          "source_schema": "default",
          "source_table": "custom_report",
          "destination_catalog": "<destination-catalog>",
          "destination_schema": "<destination-schema>",
          "destination_table": "monthly_revenue_by_channel",
          "connector_options": {
            "api_source_connector_options": {
              "options": {
                "property_id": "<property-id>",
                "dimensions": "[\"sessionDefaultChannelGroup\"]",
                "metrics": "[\"totalRevenue\", \"transactions\"]",
                "cursor_dimension": "yearMonth"
              }
            }
          }
        }
      }
    ]
  }
}
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_analytics_aggregated_job:
      name: google_analytics_aggregated_job
      schedule:
        quartz_cron_expression: '0 0 0 * * ?'
        timezone_id: 'UTC'
      tasks:
        - task_key: google_analytics_aggregated_ingestion
          pipeline_task:
            pipeline_id: ${resources.pipelines.google_analytics_aggregated_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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