{"id":22793235,"url":"https://github.com/fivetran/dbt_linkedin","last_synced_at":"2025-10-28T06:35:03.594Z","repository":{"id":42388419,"uuid":"279995085","full_name":"fivetran/dbt_linkedin","owner":"fivetran","description":"Fivetran's Linkedin Ads dbt 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LinkedIn Ad Analytics dbt Package ([docs](https://fivetran.github.io/dbt_linkedin/))\n\n\u003cp align=\"left\"\u003e\n    \u003ca alt=\"License\"\n        href=\"https://github.com/fivetran/dbt_linkedin/blob/main/LICENSE\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/License-Apache%202.0-blue.svg\" /\u003e\u003c/a\u003e\n    \u003ca alt=\"dbt-core\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/dbt_Core™_version-\u003e=1.3.0_,\u003c2.0.0-orange.svg\" /\u003e\u003c/a\u003e\n    \u003ca alt=\"Maintained?\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Maintained%3F-yes-green.svg\" /\u003e\u003c/a\u003e\n    \u003ca alt=\"PRs\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Contributions-welcome-blueviolet\" /\u003e\u003c/a\u003e\n    \u003ca alt=\"Fivetran Quickstart Compatible\"\n        href=\"https://fivetran.com/docs/transformations/dbt/quickstart\"\u003e\n        \u003cimg src=\"https://img.shields.io/badge/Fivetran_Quickstart_Compatible%3F-yes-green.svg\" /\u003e\u003c/a\u003e\n\u003c/p\u003e\n\n## What does this dbt package do?\n- Produces modeled tables that leverage Linkedin Ad Analytics data from [Fivetran's connector](https://fivetran.com/docs/applications/linkedin-ads) in the format described by [this ERD](https://fivetran.com/docs/applications/linkedin-ads#schemainformation).\n- Enables you to better understand the performance of your ads across varying grains:\n  - Providing an account, campaign (ad groups in other ad platforms), campaign group (campaigns in other ad platforms), creative, and utm/url level reports.\n- Materializes output models designed to work simultaneously with our [multi-platform Ad Reporting package](https://github.com/fivetran/dbt_ad_reporting).\n- Generates a comprehensive data dictionary of your source and modeled Linkedin Ad Analytics data through the [dbt docs site](https://fivetran.github.io/dbt_linkedin/).\n\n\u003c!--section=\"linkedin_ads_transformation_model\"--\u003e\nThe following table provides a detailed list of all tables materialized within this package by default.\n\u003e TIP: See more details about these tables in the package's [dbt docs site](https://fivetran.github.io/dbt_linkedin/#!/overview?g_v=1\u0026g_e=seeds).\n\n| **Table** | **Details** |\n|-----------|-------------|\n| [`linkedin_ads__account_report`](https://fivetran.github.io/dbt_linkedin/#!/model/model.linkedin.linkedin_ads__account_report) | Represents daily performance aggregated at the account level, including `spend`, `clicks`, `impressions`, and `conversions`.\u003cbr\u003e\u003cbr\u003e**Example Analytics Questions:**\u003cul\u003e\u003cli\u003eHow does performance compare across different accounts by account manager?\u003c/li\u003e\u003cli\u003eAre currency fluctuations affecting results across markets?\u003c/li\u003e\u003c/ul\u003e |\n| [`linkedin_ads__campaign_report`](https://fivetran.github.io/dbt_linkedin/#!/model/model.linkedin.linkedin_ads__campaign_report) | Represents daily performance aggregated at the campaign level (equivalent to ad groups in other platforms), including `spend`, `clicks`, `impressions`, and `conversions`.\u003cbr\u003e\u003cbr\u003e**Example Analytics Questions:**\u003cul\u003e\u003cli\u003eWhich campaigns have the strongest engagement relative to their budget?\u003c/li\u003e\u003cli\u003eDo certain campaigns dominate impressions within a campaign group?\u003c/li\u003e\u003cli\u003eAre new campaigns ramping up as expected after launch?\u003c/li\u003e\u003c/ul\u003e |\n| [`linkedin_ads__monthly_campaign_country_report`](https://fivetran.github.io/dbt_linkedin/#!/model/model.linkedin.linkedin_ads__monthly_campaign_country_report) | Represents monthly performance aggregated at the campaign level by country, including `spend`, `clicks`, `impressions`, and `conversions`, enriched with geographic context.\u003cbr\u003e\u003cbr\u003e**Example Analytics Questions:**\u003cul\u003e\u003cli\u003eWhich countries are delivering the highest return on ad spend for each campaign?\u003c/li\u003e\u003cli\u003eAre there seasonal performance variations by geographic region?\u003c/li\u003e\u003c/ul\u003e |\n| [`linkedin_ads__monthly_campaign_region_report`](https://fivetran.github.io/dbt_linkedin/#!/model/model.linkedin.linkedin_ads__monthly_campaign_region_report) | Represents monthly performance aggregated at the campaign level by region, including `spend`, `clicks`, `impressions`, and `conversions`, enriched with geographic context.\u003cbr\u003e\u003cbr\u003e**Example Analytics Questions:**\u003cul\u003e\u003cli\u003eWhich regions are driving the most efficient campaign performance?\u003c/li\u003e\u003cli\u003eHow do regional performance trends correlate with local market conditions?\u003c/li\u003e\u003c/ul\u003e |\n| [`linkedin_ads__campaign_group_report`](https://fivetran.github.io/dbt_linkedin/#!/model/model.linkedin.linkedin_ads__campaign_group_report) | Represents daily performance aggregated at the campaign group level (equivalent to campaigns in other platforms), including `spend`, `clicks`, `impressions`, and `conversions`.\u003cbr\u003e\u003cbr\u003e**Example Analytics Questions:**\u003cul\u003e\u003cli\u003eWhich campaign groups are most efficient in terms of cost per conversion?\u003c/li\u003e\u003cli\u003eAre paused or limited-status campaign groups still accruing impressions?\u003c/li\u003e\u003cli\u003eHow does performance vary by advertising channel type across campaign groups?\u003c/li\u003e\u003c/ul\u003e |\n| [`linkedin_ads__creative_report`](https://fivetran.github.io/dbt_linkedin/#!/model/model.linkedin.linkedin_ads__creative_report) | Represents daily performance at the individual creative level (equivalent to ads in other platforms), including `spend`, `clicks`, `impressions`, and `conversions`.\u003cbr\u003e\u003cbr\u003e**Example Analytics Questions:**\u003cul\u003e\u003cli\u003eWhich creative formats are driving the lowest cost per click?\u003c/li\u003e\u003cli\u003eDo video creatives perform better than static image creatives?\u003c/li\u003e\u003cli\u003eHow do performance trends change after refreshing creative content?\u003c/li\u003e\u003c/ul\u003e |\n| [`linkedin_ads__url_report`](https://fivetran.github.io/dbt_linkedin/#!/model/model.linkedin.linkedin_ads__url_report) | Represents daily performance at the individual URL level, including `spend`, `clicks`, `impressions`, and `conversions`, enriched with creative context.\u003cbr\u003e\u003cbr\u003e**Example Analytics Questions:**\u003cul\u003e\u003cli\u003eWhich landing pages are driving the highest conversion rates?\u003c/li\u003e\u003cli\u003eAre certain URLs performing better with specific creative combinations?\u003c/li\u003e\u003c/ul\u003e |\n\nMany of the above reports are now configurable for [visualization via Streamlit](https://github.com/fivetran/streamlit_ad_reporting). Check out some [sample reports here](https://fivetran-ad-reporting.streamlit.app/ad_performance).\n\n### Example Visualizations\n\nCurious what these tables can do? The Linkedin models provide advertising performance data that can be visualized to track key metrics like spend, impressions, click-through rates, conversion rates, and return on ad spend across different campaign structures and time periods. Check out example visualizations in the [Fivetran Ad Reporting Streamlit App](https://fivetran-ad-reporting.streamlit.app/ad_performance), and see how you can use these tables in your own reporting. Below is a screenshot of an example dashboard; explore the app for more.\n\n\u003cp align=\"center\"\u003e\n  \u003ca href=\"https://fivetran-ad-reporting.streamlit.app/ad_performance\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/fivetran/dbt_linkedin/main/images/streamlit_example.png\" alt=\"Fivetran Ad Reporting Streamlit App\" width=\"100%\"\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n### Materialized Models\nEach Quickstart transformation job run materializes 25 models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as `view`, `table`, or `incremental`.\n\u003c!--section-end--\u003e\n\n## How do I use the dbt package?\n### Step 1: Prerequisites\nTo use this dbt package, you must have the following:\n- At least one Fivetran Linkedin Ad Analytics connection syncing data into your destination.\n- A **BigQuery**, **Snowflake**, **Redshift**, **PostgreSQL**, or **Databricks** destination.\n\n#### Databricks Dispatch Configuration\nIf 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.\n```yml\ndispatch:\n  - macro_namespace: dbt_utils\n    search_order: ['spark_utils', 'dbt_utils']\n```\n\n### Step 2: Install the package (skip if also using the `ad_reporting` combination package)\nInclude the following Linkedin Ads package version in your `packages.yml` file:\n\u003e TIP: Check [dbt Hub](https://hub.getdbt.com/) for the latest installation instructions or [read the dbt docs](https://docs.getdbt.com/docs/package-management) for more information on installing packages\n```yml\n# packages.yml\npackages:\n  - package: fivetran/linkedin\n    version: [\"\u003e=1.0.0\", \"\u003c1.1.0\"]\n```\n\n\u003e All required sources and staging models are now bundled into this transformation package. Do not include `fivetran/linkedin_source` in your `packages.yml` since this package has been deprecated.\n\n### Step 3: Define database and schema variables\nBy 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:\n\n```yml\n# dbt_project.yml\nvars:\n    linkedin_ads_schema: your_schema_name\n    linkedin_ads_database: your_destination_name \n```\n\n### (Optional) Step 4: Additional configurations\n#### Union multiple connections\nIf you have multiple linkedin connections 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:\n\n```yml\nvars:\n    linkedin_ads_union_schemas: ['linkedin_usa','linkedin_canada'] # use this if the data is in different schemas/datasets of the same database/project\n    linkedin_ads_union_databases: ['linkedin_usa','linkedin_canada'] # use this if the data is in different databases/projects but uses the same schema name\n```\n\u003e NOTE: The native `src_linkedin.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 `src_linkedin.yml`.\n\nTo connect your multiple schema/database sources to the package models, follow the steps outlined in the [Union Data Defined Sources Configuration](https://github.com/fivetran/dbt_fivetran_utils/tree/releases/v0.4.latest#union_data-source) 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.\n\n#### Disable Country and Region Reports\nThis package leverages the `geo`, `monthly_ad_analytics_by_member_country`, and `monthly_ad_analytics_by_member_region` tables to help report on campaign performance by country and region. However, if you are not actively syncing these reports from your LinkedIn Ads connection, you may disable relevant transformations by adding the following variable configuration to your root `dbt_project.yml` file:\n```yml\nvars:\n    linkedin_ads__using_geo: False # True by default\n    linkedin_ads__using_monthly_ad_analytics_by_member_country: False # True by default\n    linkedin_ads__using_monthly_ad_analytics_by_member_region: False # True by default\n```\n\n#### Switching to Local Currency\nAdditionally, 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:\n\n```yml\n# dbt_project.yml\nvars:\n    linkedin_ads__use_local_currency: True # false by default -- uses USD\n```\n\n**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.\n\n#### Passing Through Additional Metrics\nBy 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`, `ad_analytics_by_creative`, `monthly_ad_analytics_by_member_country`, and `monthly_ad_analytics_by_member_region` 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:\n\n```yml\n# dbt_project.yml\nvars:\n    linkedin_ads__campaign_passthrough_metrics: # pulls from ad_analytics_by_campaign\n        - name: \"new_custom_field\"\n          alias: \"custom_field_alias\"\n          transform_sql: \"coalesce(custom_field_alias, 0)\" # reference the `alias` here if you are using one\n        - name: \"unique_int_field\"\n          alias: \"field_id\"\n        - name: \"another_one\"\n          transform_sql: \"coalesce(another_one, 0)\" # reference the `name` here if you're not using an alias\n        - name: \"that_field\"\n    linkedin_ads__creative_passthrough_metrics: # pulls from ad_analytics_by_creative\n        - name: \"new_custom_field\"\n          alias: \"custom_field\"\n        - name: \"unique_int_field\"\n    linkedin_ads__monthly_ad_analytics_by_member_country_passthrough_metrics: # pulls from monthly_ad_analytics_by_member_country\n        - name: \"country_custom_field\"\n          alias: \"country_field\"\n    linkedin_ads__monthly_ad_analytics_by_member_region_passthrough_metrics: # pulls from monthly_ad_analytics_by_member_region\n        - name: \"region_custom_field\"\n          alias: \"region_field\"\n        - name: \"region_field_two\"\n```\n\n\u003e**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.)\n\n#### Adding in Conversion Fields Variable\nSeparate 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 within the `ad_analytics_by_campaign`, `ad_analytics_by_creative`, `monthly_ad_analytics_by_member_country` and `monthly_ad_analytics_by_member_region` data models.\n\nBy default, the data models consider `external_website_conversions` and `one_click_leads` to be conversions. These should cover most use cases, but if you wanted to adjust this to your business case, you would apply the following configuration with the **original** source names of the conversion fields (not aliases you provided in the section above):\n\n```yml\n# dbt_project.yml\nvars:\n    linkedin_ads__conversion_fields: ['external_website_pre_click_conversions', 'one_click_leads', 'external_website_post_click_conversions', 'landing_page_clicks']\n```\n\nMake sure to follow best practices in configuring fields in the conversion field variables! [See the DECISIONLOG for more details](https://github.com/fivetran/dbt_linkedin/blob/main/DECISIONLOG.md#best-practices-with-configuring-linkedin-ads-conversion-fields-variable).\n\n\u003e We introduced support for conversion fields in our `report` data models in the [v0.9.0 release](https://github.com/fivetran/dbt_linkedin/releases/tag/v0.9.0) 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.\n\n#### Changing the Build Schema\nBy default this package will build the LinkedIn Ad Analytics staging models within a schema titled (\u003ctarget_schema\u003e + `_linkedin_ads_source`) and the LinkedIn Ad Analytics final models within a schema titled (\u003ctarget_schema\u003e + `_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:\n\n```yml\n# dbt_project.yml\nmodels:\n    linkedin:\n      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.\n      staging:\n        +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.\n```\n\n#### Change the source table references\nIf 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 connections.\n\n\u003e IMPORTANT: See this project's [`dbt_project.yml`](https://github.com/fivetran/dbt_linkedin/blob/main/dbt_project.yml) variable declarations to see the expected names.\n\n```yml\n# dbt_project.yml\nvars:\n    linkedin_ads_\u003cdefault_source_table_name\u003e_identifier: your_table_name \n```\n\n### (Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™\n\u003cdetails\u003e\u003csummary\u003eExpand for more details\u003c/summary\u003e\n\nFivetran offers the ability for you to orchestrate your dbt project through [Fivetran Transformations for dbt Core™](https://fivetran.com/docs/transformations/dbt). Learn how to set up your project for orchestration through Fivetran in our [Transformations for dbt Core™ setup guides](https://fivetran.com/docs/transformations/dbt#setupguide).\n\n\u003c/details\u003e\n\n## Does this package have dependencies?\nThis 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](https://hub.getdbt.com/) site.\n\u003e 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.\n```yml\npackages:\n    - package: fivetran/fivetran_utils\n      version: [\"\u003e=0.4.0\", \"\u003c0.5.0\"]\n    - package: dbt-labs/dbt_utils\n      version: [\"\u003e=1.0.0\", \"\u003c2.0.0\"]\n    - package: dbt-labs/spark_utils\n      version: [\"\u003e=0.3.0\", \"\u003c0.4.0\"]\n```\n\n## How is this package maintained and can I contribute?\n### Package Maintenance\nThe Fivetran team maintaining this package _only_ maintains the latest version of the package. We highly recommend you stay consistent with the [latest version](https://hub.getdbt.com/fivetran/linkedin/latest/) of the package and refer to the [CHANGELOG](https://github.com/fivetran/dbt_linkedin/blob/main/CHANGELOG.md) and release notes for more information on changes across versions.\n\n### Contributions\nA small team of analytics engineers at Fivetran develops these dbt packages. However, the packages are made better by community contributions.\n\nWe highly encourage and welcome contributions to this package. Check out [this dbt Discourse article](https://discourse.getdbt.com/t/contributing-to-a-dbt-package/657) on the best workflow for contributing to a package.\n\n#### Contributors\nWe thank [everyone](https://github.com/fivetran/dbt_linkedin/graphs/contributors) who has taken the time to contribute. Each PR, bug report, and feature request has made this package better and is truly appreciated.\n\nA special thank you to [Seer Interactive](https://www.seerinteractive.com/?utm_campaign=Fivetran%20%7C%20Models\u0026utm_source=Fivetran\u0026utm_medium=Fivetran%20Documentation), who we closely collaborated with to introduce native conversion support to our Ad packages.\n\n## Are there any resources available?\n- If you have questions or want to reach out for help, see the [GitHub Issue](https://github.com/fivetran/dbt_linkedin/issues/new/choose) section to find the right avenue of support for you.\n- 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](https://www.surveymonkey.com/r/DQ7K7WW).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffivetran%2Fdbt_linkedin","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffivetran%2Fdbt_linkedin","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffivetran%2Fdbt_linkedin/lists"}