Google Ads Source dbt Package (Docs)

📣 What does this dbt package do?

• Materializes Google Ads staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Google Ads data from Fivetran's connector for analysis by doing the following:
  • Name columns for consistency across all packages and for easier analysis
  • Adds freshness tests to source data
  • Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
• Generates a comprehensive data dictionary of your google_ads data through the dbt docs site.
• These tables are designed to work simultaneously with our Google Ads transformation package.

🎯 How do I use the dbt package?

Step 1: Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran Google Ads connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Databricks Dispatch Configuration

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 as well as the calogica/dbt_expectations then the google_ads_source packages respectively.

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

  - macro_namespace: dbt_expectations
    search_order: ['google_ads_source', 'dbt_expectations']

Step 2: Install the package (skip if also using the google_ads transformation package)

If you are not using the Google Ads transformation package, include the following package version in your packages.yml file. If you are installing the transform package, the source package is automatically installed as a dependency.

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

packages:
  - package: fivetran/google_ads_source
    version: [">=0.9.0", "<0.10.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

By default, this package runs using your destination and the google_ads schema. If this is not where your Google Ads data is (for example, if your google_ads schema is named google_ads_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    google_ads_database: your_destination_name
    google_ads_schema: your_schema_name 

(Optional) Step 4: Additional configurations

Expand for 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 below configurations to your dbt_project.yml file. These variables allow for the pass-through fields to be aliased (alias) if desired, but not required. Use the below format for declaring the respective pass-through variables:

Note Please ensure you exercised due diligence when adding metrics to these models. The metrics added by default (taps, impressions, and spend) have been vetted by the Fivetran team maintaining this package for accuracy. There are metrics included within the source reports, for example metric averages, which may be inaccurately represented at the grain for reports created in this package. You will want to ensure whichever metrics you pass through are indeed appropriate to aggregate at the respective reporting levels provided in this package.

vars:
    google_ads__account_stats_passthrough_metrics: 
      - name: "new_custom_field"
        alias: "custom_field"
    google_ads__campaign_stats_passthrough_metrics:
      - name: "this_field"
    google_ads__ad_group_stats_passthrough_metrics:
      - name: "unique_string_field"
        alias: "field_id"
    google_ads__keyword_stats_passthrough_metrics:
      - name: "that_field"
    google_ads__ad_stats_passthrough_metrics:
      - name: "other_id"
        alias: "another_id"

Change the build schema

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

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

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

vars:
    google_ads_<default_source_table_name>_identifier: your_table_name 

(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for more details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core™ setup guides.

🔍 Does this package have dependencies?

This dbt package is dependent on the following dbt packages. Please be aware that these dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

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

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

    - package: calogica/dbt_expectations
      version: [">=0.8.0", "<0.9.0"]

    - package: calogica/dbt_date
      version: [">=0.7.0", "<0.8.0"]

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

🙌 How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend that you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Opinionated Decisions

In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on a few different questions we came across during development. We've consolidated significant choices we made in the DECISIONLOG.md, and will continue to update as the package evolves. We are always open to and encourage feedback on these choices, and the package in general.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions!

We highly encourage and welcome contributions to this package. Check out this dbt Discourse article to learn how to contribute to a dbt package!

🏪 Are there any resources available?

• If you have questions or want to reach out for help, please refer to the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.
• Have questions or want to just say hi? Book a time during our office hours on Calendly or email us at [email protected].