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.
model | description |
---|---|
linkedin__ad_adapter | Each record represents the daily ad performance of each creative, including information about the used UTM parameters. |
linkedin__account_ad_report | Each record represents the daily ad performance of each account. |
linkedin__campaign_ad_report | Each record represents the daily ad performance of each campaign. |
linkedin__campaign_group_ad_report | Each record represents the daily ad performance of each campaign group. |
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
- Provide feedback on our existing dbt packages or what you’d like to see next
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- 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
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- Check out the dbt blog for the latest news on dbt’s development and best practices