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 Twitter 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.
|twitter__ad_adapter||Each record represents the daily ad performance of each creative, including information about the used UTM parameters.|
|twitter__line_item_report||Each record represents the daily ad performance of each line item.|
|twitter__campaign_report||Each record represents the daily ad performance of each campaign.|
Include in your
packages: - package: fivetran/twitter_ads version: [">=0.4.0", "<0.5.0"]
By default, this package will look for your Twitter Ads data in the
twitter_ads schema of your target database. If this is not where your Twitter Ads data is, please add the following configuration to your
... config-version: 2 vars: twitter_ads_schema: your_schema_name twitter_ads_database: your_database_name
For additional configurations for the source models, visit the Twitter Ads source package.
Changing the Build Schemalink
By default this package will build the Twitter Ads staging models within a schema titled (<target_schema> +
_stg_twitter_ads) and the Twitter Ads final models with a schema titled (<target_schema> +
_twitter_ads) in your target database. If this is not where you would like your modeled Twitter Ads data to be written to, add the following configuration to your
... models: twitter_ads: +schema: my_new_schema_name # leave blank for just the target_schema twitter_ads_source: +schema: my_new_schema_name # leave blank for just the target_schema
This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.
Databricks Dispatch Configurationlink
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.
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
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