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Ingest data from Google Ads

Beta

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

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

    • 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 Ads, you must complete the steps in Create a Google Ads connection.

Create an ingestion pipeline​

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:

    Bash
    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:

    Bash
    databricks bundle deploy

Examples​

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

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

YAML
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:

YAML
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:

YAML
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}

Declarative Automation Bundles job definition file​

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.

YAML
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}

Custom reports​

In addition to prebuilt tables, you can define custom report tables. A custom report lets you choose a Google Ads resource and any combination of resource fields, segments, and metrics. The connector synthesizes the equivalent Google Ads Query Language (GAQL) query, validates it against Google Ads at pipeline planning time, and ingests the result into a destination table you name.

A custom report is declared on a table-type object whose source_table is set to custom_report, with the report definition provided under connector_options.google_ads_options.custom_report_options.

Example: a custom ad_group_ad report

JSON
{
  "table": {
    "source_schema": "<customer-account-id>",
    "source_table": "custom_report",
    "destination_catalog": "<destination-catalog>",
    "destination_schema": "<destination-schema>",
    "destination_table": "my_ad_group_ad_report",
    "connector_options": {
      "google_ads_options": {
        "manager_account_id": "<google-ads-manager-account-id>",
        "sync_start_date": "2024-01-01",
        "lookback_window_days": 30,
        "custom_report_options": {
          "resource": "ad_group_ad",
          "resource_fields": ["ad_group_ad.ad.id", "ad_group_ad.status"],
          "segments": ["segments.date", "segments.device"],
          "metrics": ["metrics.impressions", "metrics.clicks", "metrics.cost_micros", "metrics.conversions"]
        }
      }
    }
  }
}

destination_table is required when source_table is custom_report.

For custom report configuration options, see Custom report configuration options.

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​

On this page