This package contains staging models, designed to work simultaneously with our Snapchat Ads transform package and our multi-platform Ad Reporting package. The staging models name columns consistently across all packages:
- Boolean fields are prefixed with
- Timestamps are appended with
- ID primary keys are prefixed with the name of the table. For example, the campaign table’s ID column is renamed
Include in your
packages: - package: fivetran/snapchat_ads_source 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
Changing the Build Schemalink
By default, this package will build the Snapchat Ads staging models within a schema titled (
_stg_snapchat_ads) in your target database. If this is not where you would like your Snapchat Ads staging data to be written to, add the following configuration to your
... models: 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']
- 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 firstname.lastname@example.org.
- 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