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 Snapchat 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.
|snapchat__ad_adapter||Each record represents the daily ad performance of each ad, including information about the used UTM parameters.|
|snapchat__account_report||Each record represents the daily ad performance of each account.|
|snapchat__campaign_report||Each record represents the daily ad performance of each campaign.|
|snapchat__ad_squad_report||Each record represents the daily ad performance of each ad squad.|
Include in your
packages: - package: fivetran/snapchat_ads version: [">=0.3.0", "<0.4.0"]
By default, this package will look for your Snapchat Ads data in the
snapchat_ads schema of your target database. If this is not where your Snapchat Ads data is, please add the following configuration to your
... config-version: 2 vars: snapchat_schema: your_schema_name snapchat_database: your_database_name
For additional configurations for the source models, visit the Snapchat Ads source package.
Changing the Build Schemalink
By default, this package will build the Snapchat Ads staging models within a schema titled (
_stg_snapchat_ads) and the final Snapchat Ads models within a schema titled (
_snapchat_ads) in your target database. If this is not where you would like your modeled Snapchat data to be written to, add the following configuration to your
... models: snapchat_ads: +schema: my_new_schema_name # leave blank for just the target_schema snapchat_ads_source: +schema: my_new_schema_name # leave blank for just the target_schema
Don’t see a model or specific metric you would have liked to be included? Notice any bugs when installing and running the package? If so, we highly encourage and welcome contributions to this package!
Please create issues or open PRs against
master. See the Discourse post for information on how to contribute to a package.
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']
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