Ingest data from Google Ads

Important

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

Learn how to create a managed ingestion pipeline to ingest data from Google Ads into Azure Databricks.

Requirements

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

    • Your workspace must be enabled for Unity Catalog.

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

    • If you plan 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.

    • If you plan 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 Ads, you must complete the steps in Configure OAuth for Google Ads ingestion.

Create an ingestion pipeline

Declarative Automation Bundles

This tab describes how to deploy an ingestion pipeline using Declarative Automation Bundles. 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?.

  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_ads_pipeline.yml).
    • A job definition file that controls the frequency of data ingestion (for example, resources/google_ads_job.yml).

    See pipeline.ingestion_definition and Examples.

  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 cell one as-is.

  3. Modify cell two or three with your pipeline configuration details, depending on your use case. See pipeline.ingestion_definition and Examples.

  4. Click Run all.

Examples

Declarative Automation Bundles

The manager_account_id option is not supported in Declarative Automation Bundles. To configure a manager account ID, use an Azure Databricks notebook instead.

The following pipeline definition file ingests all current and future tables from one account:

resources:
  pipelines:
    pipeline_google_ads:
      name: <pipeline-name>
      catalog: <destination-catalog>
      target: <destination-schema>
      ingestion_definition:
        connection_name: <connection-name>
        objects:
          - schema:
              source_schema: <account-id>
              destination_catalog: <destination-catalog>
              destination_schema: <destination-schema>
              connector_options:
                google_ads_options:
                  lookback_window_days: <lookback-window-days>
                  sync_start_date: <sync-start-date>

The following pipeline definition file selects specific tables from an account to ingest:

resources:
  pipelines:
    pipeline_google_ads:
      name: <pipeline-name>
      catalog: <destination-catalog>
      target: <destination-schema>
      ingestion_definition:
        connection_name: <connection-name>
        objects:
          - table:
            source_schema: <customer-account-id>
            source_table: <table1>
            destination_catalog: <destination-catalog>
            destination_schema: <destination-schema>
            destination_table: <destination-table>
            connector_options:
              google_ads_options:
                lookback_window_days: <lookback-window-days>
                sync_start_date: <sync-start-date>
          - table:
            source_schema: <customer-account-id>
            source_table: table2
            destination_catalog: <destination-catalog>
            destination_schema: <destination-schema>
            destination_table: <destination-table>
            connector_options:
              google_ads_options:
                lookback_window_days: <lookback-window-days>
                sync_start_date: <sync-start-date>

The following is an example job definition file:

resources:
  jobs:
    google_ads_dab_job:
      name: google_ads_dab_job
      trigger:
        # Run this job every day, exactly one day from the last run
        # See https://docs.databricks.com/api/workspace/jobs/create#trigger
        periodic:
          interval: 1
          unit: DAYS
      email_notifications:
        on_failure:
          - <email-address>
      tasks:
        - task_key: refresh_pipeline
          pipeline_task:
            pipeline_id: ${resources.pipelines.pipeline_google_ads.id}

Databricks notebook

The following pipeline specification ingests all current and future tables from one account:

pipeline_spec = {
  "name": "<pipeline-name>",
  "catalog": "<destination-catalog>",
  "schema": "<destination-schema>",
  "ingestion_definition": {
    "connection_name": "<connection-name>",
    "objects": [
      {
        "schema": {
          "source_schema": "<account-id>",
          "destination_catalog": "<destination-catalog>",
          "destination_schema": "<destination-schema>",
          "connector_options": {
            "google_ads_options": {
              "manager_account_id": "<manager-account-id>",
              "lookback_window_days": "<lookback-window-days>",
              "sync_start_date": "<sync-start-date>"
            }
          }
        }
      }
    ]
  }
}

json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

The following pipeline specification selects specific tables from an account to ingest:

pipeline_spec = {
  "name": "<pipeline-name>",
  "catalog": "<destination-catalog>",
  "schema": "<destination-schema>",
  "ingestion_definition": {
    "connection_name": "<connection-name>",
    "objects": [
      {
        "table": {
          "source_schema": "<customer-account-id>",
          "source_table": "<table1>",
          "destination_catalog": "<destination-catalog>",
          "destination_schema": "<destination-schema>",
          "destination_table": "<destination-table>",
          "connector_options": {
            "google_ads_options": {
              "manager_account_id": "<manager-account-id>",
              "lookback_window_days": "<lookback-window-days>",
              "sync_start_date": "<sync-start-date>"
            }
          }
        }
      },
      {
        "table": {
          "source_schema": "<customer-account-id>",
          "source_table": "<table2>",
          "destination_catalog": "<destination-catalog>",
          "destination_schema": "<destination-schema>",
          "destination_table": "<destination-table>",
          "connector_options": {
            "google_ads_options": {
              "manager_account_id": "<manager-account-id>",
              "lookback_window_days": "<lookback-window-days>",
              "sync_start_date": "<sync-start-date>"
            }
          }
        }
      }
    ]
  }
}

json_payload = json.dumps(pipeline_spec, indent=2)
create_pipeline(json_payload)

Declarative Automation Bundles job definition file

Declarative Automation Bundles

The following is an example job definition file to use with Declarative Automation Bundles. The job runs every day, exactly one day from the last run.

resources:
  jobs:
    google_ads_dab_job:
      name: google_ads_dab_job

      trigger:
        periodic:
          interval: 1
          unit: DAYS

      email_notifications:
        on_failure:
          - <email-address>

      tasks:
        - task_key: refresh_pipeline
          pipeline_task:
            pipeline_id: ${resources.pipelines.pipeline_google_ads.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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