This package enriches your Fivetran data by doing the following:
- Adds descriptions to tables and columns that are synced using Fivetran
- Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
- Models staging tables, which will be used in our transform package
This package contains staging models, designed to work simultaneously with our Microsoft Advertising transformation 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/microsoft_ads_source version: [">=0.5.0", "<0.6.0"]
By default, this package looks for your Microsoft Advertising data in the
bingads schema of your target database. If this is not where your Microsoft Advertising data is, add the following configuration to your
... config-version: 2 vars: microsoft_ads_schema: your_schema_name microsoft_ads_database: your_database_name
Changing the Build Schemalink
By default this package will build the Microsoft Ads staging models within a schema titled (<target_schema> +
_stg_microsoft_ads) in your target database. If this is not where you would like your Microsoft Ads staging data to be written to, add the following configuration to your
... models: microsoft_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
on the best workflow for contributing to a package.
- 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 shoot us an email 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
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