Google Ads (Source)

This package models Google Ads data from Fivetran's connector.

Models

This package contains staging models, designed to work simultaneously with our Google Ads modeling package and our multi-platform Ad Reporting package. The staging models name columns consistently across all packages:

• Boolean fields are prefixed with is_ or has_
• Timestamps are appended with _timestamp
• ID primary keys are prefixed with the name of the table. For example, the campaign table's ID column is renamed campaign_id.

Installation Instructions

Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.

Include in your packages.yml

packages:
  - package: fivetran/google_ads_source
    version: [">=0.4.0", "<0.5.0"]

Configuration

This package allows users to leverage either the Adwords API or the Google Ads API. You will be able to determine which API your connector is using by navigating within your Fivetran UI to the setup tab -> edit connection details link -> and reference the API configuration used. You will want to refer to the respective configuration steps below based off the API used by your connector.

Google Ads API Configuration

If your connector is setup using the Google Ads API then you will need to configure your dbt_project.yml with the below variable:

# dbt_project.yml

...
config-version: 2

vars:
    api_source: google_ads  ## adwords by default

Adwords API Configuration

If your connector is setup using the Adwords API then you will need to pull the following custom reports through Fivetran:

• Destination Table Name: final_url_performance

• Report Type: FINAL_URL_REPORT

• Fields:

  • AccountDescriptiveName
  • AdGroupId
  • AdGroupName
  • AdGroupStatus
  • CampaignId
  • CampaignName
  • CampaignStatus
  • Clicks
  • Cost
  • Date
  • EffectiveFinalUrl
  • ExternalCustomerId
  • Impressions

• Destination Table Name: criteria_performance

• Report Type: CRITERIA_PERFORMANCE_REPORT

• Fields:

  • AccountDescriptiveName
  • AdGroupId
  • AdGroupName
  • AdGroupStatus
  • CampaignId
  • CampaignName
  • CampaignStatus
  • Clicks
  • Cost
  • Criteria
  • CriteriaDestinationUrl
  • CriteriaType
  • Date
  • ExternalCustomerId
  • Id
  • Impressions

• Destination Table Name: click_performance

• Report Type: CLICK_PERFORMANCE_REPORT

• Fields:

  • AccountDescriptiveName
  • AdGroupId
  • AdGroupName
  • AdGroupStatus
  • CampaignId
  • CampaignName
  • CampaignStatus
  • Clicks
  • CriteriaId
  • Date
  • ExternalCustomerId
  • GclId

The package assumes that the corresponding destination tables are named final_url_performance, criteria_performance, and click_performance respectively. If these tables have different names in your destination, enter the correct table names in the google_ads__final_url_performance, google_ads__click_performance, and google_ads__criteria_performance variables so that the package can find them:

# dbt_project.yml

...
config-version: 2

vars:
    google_ads__final_url_performance: "{{ ref('a_model_you_wrote') }}"
    google_ads__click_performance: adwords.click_performance_report

Source Schema is Named Differently

By default, this package will look for your Google Ads data in the adwords or google_ads schema of your target database, depending on which API source you are using. If this is not where your Google Ads data is, please add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    google_ads_schema: your_schema_name
    google_ads_database: your_database_name

Optional Configurations

Passing Through Additional Metrics

By default, this package will select clicks, impressions, and cost from the source reporting tables to store into the staging models. If you would like to pass through additional metrics to the staging models, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
vars:
    # If you're using the Adwords API source
    google_ads__url_passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from adwords.final_url_performance
    google_ads__criteria_passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from adwords.criteria_performance

    # If you're using the Google Ads API source
    google_ads__ad_stats_passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from google_ads.ad_stats

UTM Auto Tagging Feature

This package assumes you are manually adding UTM tags to the EffectiveFinalUrl field within the FINAL_URL_REPORT table. If you are leveraging the auto-tag feature within Google Ads then you will want to enable the google_auto_tagging_enabled variable to correctly populate the UTM fields within the stg_google_ads__final_url_performance model.

vars:
    google_ads_source:
      google_auto_tagging_enabled: true # False by default

Changing the Build Schema

By default this package will build the Google Ads staging models within a schema titled (<target_schema> + _stg_google_ads) in your target database. If this is not where you would like your Google Ads staging data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
models:
    google_ads_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

Database Support

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.

Databricks Dispatch Configuration

dbt v0.20.0 introduced a new project-level dispatch configuration that enables an "override" setting for all dispatched macros. If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

# dbt_project.yml

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Contributions

Additional contributions to this package are very welcome! Please create issues or open PRs against main. Check out this post on the best workflow for contributing to a package.

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