These packages are under active development and are expected to change with dbt metrics as it evolves over time. As of now, dbt metrics requires users to define models to calculate metrics and these models are persisted on the warehouse. Keeping this in mind, we have currently modelled our packages such that metrics and the models calculating these metrics have a 1:1 mapping, which is why you will see multiple metrics for the same conceptual metric entity accounting for different time grains and dimensions. In future, with the roll out of dbt Server and evolution of dbt metrics, we expect to streamline our packages to remove these redundancies.

The metrics in these packages are transformed on top of source data ETL'd via Fivetran to your warehouse. Make sure you have connected your SaaS source with Fivetran for the packages to work properly.

This package provides pre-built metrics for Google ads data from Fivetran's connector. It uses data in the format described by this ERD.

This package enables you to access commonly used metrics on top of Google Ads Data

This package contains transformed models built on top of google_ads_source package. A dependency on the source packages is declared in this package's packages.yml file, so it will automatically download when you run dbt deps.

The metrics offered by this package are described below. Note that some metrics contain extended metrics for segmentation based on adgroups, campaigns, and accounts.

Please refer to the following notes regarding nuances related to the data available from the package:

• The latest attributes for account, campaign, adgroup and ad (like name, status) are being fetched from the Fivetran source tables. To change this, you can edit the dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
  is_most_recent_record: 'FALSE'

• The calculation of monthly ratio metrics is done at an aggregate level. For example, the Monthly Cost per Click metric for campaigns is calculated as Total Spend (across all campaigns) / Total Clicks (across all campaigns)
• The calculation for average ratio metrics is done by taking the average of the ad-level ratio metrics at the campaign level. For example, the Monthly Average Cost per Click metric is calculated as Avg (Total Spend per Ad) / Avg (Total clicks per Ad) ; this number is reported per campaign

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, or PostgreSQL destination.

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:
  - git: "https://github.com/HousewareHQ/dbt_google_ads_metrics.git"
    revision: v0.1.0

By default, this package will look for your Google Ads data in the fivetran_google_ads schema of your target database. 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_database: your_database_name
  google_ads_schema: your_schema_name

For additional configurations for the source models, please visit the Google Ads source package.

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

# dbt_project.yml

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

This package has been tested on Snowflake.

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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