LinkedIn Ad Analytics ([docs](https://fivetran-dbt-linkedin.netlify.app/#!/overview))

This package models LinkedIn Ad Analytics 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.

Modelslink

This package contains transformation models, designed to work simultaneously with our LinkedIn Ad Analytics 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.

Installation Instructionslink

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/linkedin
    version: [">=0.4.0", "<0.5.0"]

Configurationlink

By default, this package will look for your LinkedIn Ad Analytics data in the linkedin_ads schema of your target database. If this is not where your LinkedIn Ad Analytics data is, please add the following configuration to your dbt_project.yml file:

...
config-version: 2

vars:
    linkedin_database: your_database_name
    linkedin_schema: your_schema_name

For additional configurations for the source models, please visit the LinkedIn Ad Analytics source package.

Switching to Local Currencylink

Additionally, the package allows you to select whether you want to add in costs in USD or the local currency of the ad. By default, the package uses USD. If you would like to have costs in the local currency, add the following variable to your dbt_project.yml file:

...
config-version: 2

vars:
    linkedin__use_local_currency: True

Passing Through Additional Metricslink

By default, this package will select clicks, impressions, and costs from the source ad_analytics_by_creative table to store into the linkedin__ad_adapter model. If you would like to pass through additional metrics to the ad adapter model, add the following configuration to your dbt_project.yml file:

...
vars:
    linkedin__passthrough_metrics: ['the', 'list', 'of', 'metric', 'columns', 'to', 'include'] # from LINKEDIN_ADS.AD_ANALYTICS_BY_CREATIVE

Changing the Build Schemalink

By default this package will build the LinkedIn Ad Analytics staging models within a schema titled (<target_schema> + _stg_linkedin) and the LinkedIn Ad Analytics final models within a schema titled (<target_schema> + _linkedin) in your target database. If this is not where you would like your modeled LinkedIn data to be written to, add the following configuration to your dbt_project.yml file:

...
models:
    linkedin:
      +schema: my_new_schema_name # leave blank for just the target_schema
    linkedin_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

Contributionslink

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.

Database Supportlink

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.

Databricks Dispatch Configurationlink

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.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Resources:link

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