LinkedIn Ad Analytics Source dbt Package (docs)

What does this dbt package do?

• Materializes Linkedin Ads Analytics staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Linkedin Ads Analytics data from Fivetran's connector for analysis by doing the following:
  • Name columns for consistency across all packages and for easier analysis
  • Adds freshness tests to source data
  • Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
• Generates a comprehensive data dictionary of your Linkedin Ad Analytics data through the dbt docs site.
• These tables are designed to work simultaneously with our Linkedin Ads transformation package.

How do I use the dbt package?

Step 1: Prerequisites

To use this dbt package, you must have the following:

• At least one Fivetran Linkedin Ad Analytics connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Databricks Dispatch Configuration

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.

# dbt_project.yml
dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Step 2: Install the package (skip if also using the linkedin transformation package, or ad_reporting combination package)

If you are not using the Linkedin transformation package and/or Ad Reporting transformation package, include the following package version in your packages.yml file. If you are installing the transform package, the source package is automatically installed as a dependency.

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.

# packages.yml
packages:
  - package: fivetran/linkedin_source
    version: [">=0.9.0", "<0.10.0"]

Step 3: Define database and schema variables

By default, this package runs using your destination and the linkedin_ads schema. If this is not where your Linkedin Ads data is (for example, if your linkedin schema is named linkedin_ads_fivetran), add the following configuration to your root dbt_project.yml file:

# dbt_project.yml
vars:
    linkedin_ads_database: your_destination_name
    linkedin_ads_schema: your_schema_name

(Optional) Step 4: Additional configurations

Union multiple connectors

If you have multiple linkedin connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the linkedin_ads_union_schemas OR linkedin_ads_union_databases variables (cannot do both) in your root dbt_project.yml file:

vars:
    linkedin_ads_union_schemas: ['linkedin_usa','linkedin_canada'] # use this if the data is in different schemas/datasets of the same database/project
    linkedin_ads_union_databases: ['linkedin_usa','linkedin_canada'] # use this if the data is in different databases/projects but uses the same schema name

NOTE: The native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.

To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.

Switching to Local Currency for Costs

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:

# dbt_project.yml
vars:
    linkedin_ads__use_local_currency: True # false by default -- uses USD

Note: Unlike cost, conversion values are only available in the local currency. The package will only use the conversion_value_in_local_currency field for conversion values, while it may draw from the cost_in_local_currency and cost_in_usd source fields for cost.

Passing Through Additional Metrics

By default, this package will select clicks, impressions, cost and conversion_value_in_local_currency (as well as fields set via linkedin_ads__conversion_fields in the next section) from the source reporting tables ad_analytics_by_campaign and ad_analytics_by_creative to store into the corresponding staging models. If you would like to pass through additional metrics to the staging models, add the below configurations to your dbt_project.yml file. These variables allow for the pass-through fields to be aliased (alias) and transformed (transform_sql) if desired, but not required. Only the name of each metric field is required. Use the below format for declaring the respective pass-through variables:

# dbt_project.yml
vars:
    linkedin_ads__campaign_passthrough_metrics: # pulls from ad_analytics_by_campaign
        - name: "new_custom_field"
          alias: "custom_field_alias"
          transform_sql: "coalesce(custom_field_alias, 0)" # reference the `alias` here if you are using one
        - name: "unique_int_field"
          alias: "field_id"
        - name: "another_one"
          transform_sql: "coalesce(another_one, 0)" # reference the `name` here if you're not using an alias
        - name: "that_field"
    linkedin_ads__creative_passthrough_metrics: # pulls from ad_analytics_by_creative
        - name: "new_custom_field"
          alias: "custom_field"
        - name: "unique_int_field"

Note Please ensure you exercised due diligence when adding metrics to these models. The metrics added by default (clicks, impressions, and spend) have been vetted by the Fivetran team maintaining this package for accuracy. There are metrics included within the source reports, for example metric averages, which may be inaccurately represented at the grain for reports created in this package. You will want to ensure whichever metrics you pass through are indeed appropriate to aggregate at the respective reporting levels provided in this package. (Important: You do not need to add conversions in this way. See the following section for an alternative implementation.)

Adding in Conversion Fields Variable

Separate from the above passthrough metrics, the package will also include conversion metrics based on the linkedin_ads__conversion_fields variable, in addition to the conversion_value_in_local_currency field.

By default, the data models consider external_website_conversions and one_click_leads to be conversions. These should cover most use cases, but, say, if you would like to consider landing page clicks and external post click conversions to also be conversions, you would apply the following configuration with the original source names of the conversion fields (not aliases you provided in the section above):

# dbt_project.yml
vars:
    linkedin_ads__conversion_fields: ['external_website_conversions',  'one_click_leads', 'external_website_post_click_conversions', 'landing_page_clicks']

Make sure to follow best practices in configuring fields in the conversion field variables! See the DECISIONLOG for more details.

We introduced support for conversion fields in our ad_analytics_by_campaign and ad_analytics_by_creative data models in the v0.9.0 release of the package, but customers might have been bringing in these conversion fields earlier using the passthrough fields variables. The data models will avoid "duplicate column" errors automatically if this is the case.

Change the build schema

By default, this package builds the Linkedin Ads staging models within a schema titled (<target_schema> + _linkedin_ads_source) in your destination. If this is not where you would like your Linkedin staging data to be written to, add the following configuration to your root dbt_project.yml file:

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

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable. This is not available when running the package on multiple unioned connectors.

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

# dbt_project.yml
vars:
    linkedin_ads_<default_source_table_name>_identifier: your_table_name 

(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]
    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]
    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

How is this package maintained and can I contribute?

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend that you stay consistent with the latest version of the package and refer to the CHANGELOG and release notes for more information on changes across versions.

Contributions

A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.

We highly encourage and welcome contributions to this package. Check out this dbt Discourse article on the best workflow for contributing to a package.

Contributors

We thank everyone who has taken the time to contribute. Each PR, bug report, and feature request has made this package better and is truly appreciated.

A special thank you to Seer Interactive, who we closely collaborated with to introduce native conversion support to our Ad packages.

Are there any resources available?

• If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.