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 analytics-ready models, including an 'ad adapter' model that can be easily unioned in to other ad platform packages to get a single view. This is especially easy using our Ad Reporting package.
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 |
|---|---|
| facebook_ads__ad_adapter | Each record represents the daily ad performance of each creative, including information about the used UTM parameters. |
| facebook_ads__account_report | Each record represents the daily ad performance of each account. |
| facebook_ads__campaign_report | Each record represents the daily ad performance of each campaign. |
| facebook_ads__ad_set_report | Each record represents the daily ad performance of each ad set. |
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
version: [">=0.4.0", "<0.5.0"]To use this package, you will need to configure your Facebook Ads connector to pull the BASIC_AD pre-built report. Follow the below steps in the Fivetran UI to do so:
- Navigate to the connector setup form (Setup -> Edit connection details for pre-existing connectors)
- Click Add table
- Select Pre-built Report
- Set the table name to
basic_ad - Select
BASIC_ADas the corresponding pre-built report - Select a daily aggregation period
- Click Ok and Save & test!
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 Fa
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cebook 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.
By default this package will build the Facebook Ads staging models within a schema titled (<target_schema> + _stg_facebook_ads), the Facebook Creative History models within a schema titled (<target_schema> + _facebook_ads_creative_history), and the final Facebook Ads models within a schema titled (<target_schema> + _facebook_ads) 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:
+schema: my_new_schema_name # leave blank for just the target_schema
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_schemaThis package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
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']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.
- Provide feedback on our existing dbt packages or what you'd like to see next
- Find all of Fivetran's pre-built dbt packages in our dbt hub
- Learn more about Fivetran in the Fivetran 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
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- Check out the dbt blog for the latest news on dbt's development and best practices