The following table provides a detailed list of all models materialized within this package by default.
TIP: See more details about these models in the package's dbt docs site.
Model | Description |
---|---|
linkedin_ads__account_report | Each record represents the daily ad performance of each account. |
linkedin_ads__campaign_report | Each record represents the daily ad performance of each campaign. Linkedin campaigns map onto ad groups in other ad platforms. |
linkedin_ads__campaign_group_report | Each record represents the daily ad performance of each campaign group. Linkedin |
linkedin_ads__creative_report | Each record represents the daily ad performance of each creative. |
linkedin_ads__url_report | Each record represents the daily ad performance of each url. |
To use this dbt package, you must have the following:
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']
Include the following Linkedin Ads package version in your packages.yml
file:
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 version: [">=0.8.0", "<0.9.0"]
Do NOT include the
linkedin_source
package in this file. The transformation package itself has a dependency on it and will install the source package as well.
By default, this package runs using your destination and the linkedin_ads
schema. If this is not where your Linkedin Ad Analytics 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_schema: your_schema_name
linkedin_ads_database: your_destination_name
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
Please be aware that 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.
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
By default, this package will select clicks
, impressions
, and cost
from the source reporting tables to store into the 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
) if desired, but not required. Use the below format for declaring the respective pass-through variables:
Note Please ensure you exercised due diligence when adding metrics to these models. The metrics added by default (taps, 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.
# dbt_project.yml
vars:
linkedin_ads__campaign_passthrough_metrics: # pulls from ad_analytics_by_campaign
- name: "new_custom_field"
alias: "custom_field"
- name: "unique_int_field"
alias: "field_id"
- 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"
By default this package will build the LinkedIn Ad Analytics staging models within a schema titled (_linkedin_ads_source
) and the LinkedIn Ad Analytics final models within a schema titled (_linkedin_ads
) 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:
# dbt_project.yml
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
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:
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
This dbt package is dependent on the following dbt packages. Please be aware that 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 rootpackages.yml
to avoid package version conflicts.packages: - package: fivetran/linkedin_source version: [">=0.8.0", "<0.9.0"] - 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"]
The Fivetran team maintaining this package only maintains the latest version of the package. We highly recommend 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.
A small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions!