Facebook Ads Backwards Compatibility

⚠️ Warning! ⚠️

This package is no longer maintained by the Fivetran team and is not recommended for use. This package was required for an older version of the Facebook Ads dbt package. This compatibility is no longer needed and is not listed as a dependency in our other Facebook Ads packages. We recommend removing this package from your packages.yml and using the latest version of the dbt_facebook_ads package instead.

To view the current release of the dbt_facebook_ads package, please refer to the release list.

This package models Facebook Ads data from Fivetran's connector. It uses data in the format described by this ERD.

The main focus of the package is to transform the core ad object tables into models with schemas that match the old Fivetran Facebook connector.

Models

This package contains transformation models, designed to work simultaneously with our Facebook Ads source package and our multi-platform Ad Reporting package. A dependency on the source package is declared in this package's packages.yml file, so it will automatically download when you run dbt deps. The primary outputs of this package are described below.

model	description
stg_facebook_ads__app_link	Maps to the app_link table in the old connector
stg_facebook_ads__carousel_media_url_tags	Maps to the carousel_media_url_tag table in the old connector
stg_facebook_ads__carousel_media	Maps to the carousel_media table in the old connector
stg_facebook_ads__creative_history_asset_feed_spec_link_url	Maps to the creative_history_asset_feed_spec_link_url table in the old connector
stg_facebook_ads__asset_feed_spec_link_url	(Postgres Only) Maps to the creative_history_asset_feed_spec_link_url table in the old connector
stg_facebook_ads__url_tag	Maps to the url_tag table in the old connector

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/facebook_ads_creative_history
    version: [">=0.4.0", "<0.5.0"]

Configuration

By default, this package will look for your Facebook Ads data in the facebook_ads schema of your target database. If this is not where your Facebook Ads data is, please add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    facebook_ads_schema: your_schema_name
    facebook_ads_database: your_database_name 

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

Changing the Build Schema

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

# dbt_project.yml

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

Resources:

• Provide feedback on our existing dbt packages or what you'd like to see next
• Have questions or feedback, or need help? Book a time during our office hours here or email us at solutions@fivetran.com.
• Find all of Fivetran's pre-built dbt packages in our dbt hub
• Learn how to orchestrate your models with Fivetran Transformations for dbt Core™
• Learn more about Fivetran overall in our docs
• Check out Fivetran's blog
• Learn more about dbt in the dbt docs
• Check out Discourse for commonly asked questions and answers
• Join the chat on Slack for live discussions and support
• Find dbt events near you
• Check out the dbt blog for the latest news on dbt's development and best practices