Ad Reporting dbt Package (Docs)

📣 What does this dbt package do?

• Standardizes schemas from various ad platform connectors and creates reporting models for clicks, spend and impressions aggregated to the account, campaign, ad group, ad, keyword and search levels.
• Currently supports the following Fivetran ad platform connectors:
  • Amazon Ads
  • Apple Search Ads
  • Facebook Ads
  • Google Ads
  • LinkedIn Ad Analytics
  • Microsoft Advertising
  • Pinterest Ads
  • Snapchat Ads
  • TikTok Ads
  • Twitter Ads
  • Reddit Ads

NOTE: You do not need to have all of these connector types to use this package, though you should have at least two.

• Generates a comprehensive data dictionary of your source and modeled Ad Reporting data via the dbt docs site

Refer to the table below for a detailed view of final models materialized by default within this package. Additionally, check out our Docs site for more details about these models.

model	description
ad_reporting__account_report	Each record represents daily metrics by account
ad_reporting__campaign_report	Each record represents daily metrics by campaign and account.
ad_reporting__ad_group_report	Each record represents daily metrics by ad group, campaign and account.
ad_reporting__ad_report	Each record represents daily metrics by ad, ad group, campaign and account.
ad_reporting__keyword_report	Each record represents daily metrics by keyword, ad group, campaign and account.
ad_reporting__search_report	Each record represents daily metrics by search query, ad group, campaign and account.
ad_reporting__url_report	Each record represents daily metrics by URL (and if applicable, URL UTM parameters), ad group, campaign and account.

The individual platform models may have additional platform-specific metrics and fields better suited for deep-dive analyses at the platform level.

🎯 How do I use the dbt package?

Step 1: Pre-Requisites

Connector: Have at least one of the below supported Fivetran ad platform connectors syncing data into your warehouse. This package currently supports: - Amazon Ads - Apple Search Ads - Facebook Ads - Google Ads - LinkedIn Ad Analytics - Microsoft Advertising - Pinterest Ads - Snapchat Ads - TikTok Ads - Twitter Ads - Reddit Ads

While you need only one of the above connectors to utilize this package, we recommend having at least two to gain the rollup benefit of this package.

• Database support: This package has been tested on BigQuery, Snowflake, Redshift, Postgres and Databricks. Ensure you are using one of these supported databases.

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 as well as the calogica/dbt_expectations then the google_ads_source packages respectively.

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

  - macro_namespace: dbt_expectations
    search_order: ['google_ads_source', 'dbt_expectations']

Step 2: Installing the Package

Include the following github package version in your packages.yml

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

packages:
  - package: fivetran/ad_reporting
    version: [">=1.3.0", "<1.4.0"]

Step 3: Configure Database and Schema Variables

By default, this package looks for your ad platform data in your target database. If this is not where your app platform data is stored, add the relevant <connector>_database variables to your dbt_project.yml file (see below).

vars:
    amazon_ads_schema: amazon_ads
    amazon_ads_database: your_database_name

    apple_search_ads_schema: apple_search_ads
    apple_search_ads_database: your_database_name

    facebook_ads_schema: facebook_ads
    facebook_ads_database: your_database_name 

    google_ads_schema: google_ads
    google_ads_database: your_database_name 

    microsoft_ads_schema: bingads
    microsoft_ads_database: your_database_name

    linkedin_ads_schema: linkedin_ads 
    linkedin_ads_database: your_database_name  

    pinterest_schema: pinterest
    pinterest_database: your_database_name 

    twitter_ads_schema: twitter_ads
    twitter_ads_database: your_database_name  

    snapchat_schema: snapchat_ads
    snapchat_database: your_database_name 
    
    tiktok_ads_schema: tiktok_ads
    tiktok_ads_database: your_database_name

    reddit_ads_schema: reddit_ads
    reddit_ads_database: your_database_name 

Step 4: Enabling/Disabling Models

This package takes into consideration that not every account will have every feature enabled per platform. If your syncs exclude certain tables, it is because you either don't use that functionality in your respective ad platforms or have actively excluded some tables from your syncs.

Disable Platform Specific Reporting

If you would like to disable all reporting for any specific platform, please include the respective variable(s) in your dbt_project.yml.

vars:
  ad_reporting__amazon_ads_enabled: False # by default this is assumed to be True
  ad_reporting__apple_search_ads_enabled: False # by default this is assumed to be True
  ad_reporting__pinterest_ads_enabled: False # by default this is assumed to be True
  ad_reporting__microsoft_ads_enabled: False # by default this is assumed to be True
  ad_reporting__linkedin_ads_enabled: False # by default this is assumed to be True
  ad_reporting__google_ads_enabled: False # by default this is assumed to be True
  ad_reporting__twitter_ads_enabled: False # by default this is assumed to be True
  ad_reporting__facebook_ads_enabled: False # by default this is assumed to be True
  ad_reporting__snapchat_ads_enabled: False # by default this is assumed to be True
  ad_reporting__tiktok_ads_enabled: False # by default this is assumed to be True
  ad_reporting__reddit_ads_enabled: False # by default this is assumed to be True

Enable/Disable Specific Reports within Platforms

For Apple Search Ads, if you are not utilizing the search functionality, you may choose to update the respective variable below.

For Twitter Ads, if you are tracking keyword performance, you may choose to update the corresponding variable below.

Add the following variables to your dbt_project.yml file

vars:
  apple_search_ads__using_search_terms: False # by default this is assumed to be True
  twitter_ads__using_keywords: False # by default this is assumed to be True

(Recommended) Step 5: Change the Build Schema

By default this package will build all models in your <target_schema> with the respective package suffixes (see below). This behavior can be tailored to your preference by making use of custom schemas. If you would like to override the current naming conventions, please add the following configuration to your dbt_project.yml file and rename +schema configs:

models:  
  ad_reporting:
    +schema: ad_reporting

  amazon_search_ads:
    +schema: amazon_ads
  amazon_ads_source:
    +schema: amazon_ads_source

  apple_search_ads:
    +schema: apple_search_ads
  apple_search_ads_source:
    +schema: apple_search_ads_source
  
  facebook_ads:
    +schema: facebook_ads
  facebook_ads_source:
    +schema: facebook_ads_source
  
  google_ads:
    +schema: google_ads
  google_ads_source:
    +schema: google_ads_source

  linkedin:
    +schema: linkedin
  linkedin_source:
    +schema: linkedin_source

  microsoft_ads:
    +schema: microsoft_ads
  microsoft_ads_source:
    +schema: microsoft_ads_source

  pinterest:
    +schema: pinterest
  pinterest_source:
    +schema: pinterest_source
  
  twitter_ads:
    +schema: twitter_ads
  twitter_ads_source:
    +schema: twitter_ads_source
  
  snapchat_ads:
    +schema: snapchat_ads
  snapchat_ads_source:
    +schema: snapchat_ads_source
  
  tiktok_ads:
    +schema: tiktok_ads
  tiktok_ads_source:
    +schema: tiktok_ads_source
  
  reddit_ads:
    +schema: reddit_ads
  reddit_ads_source:
    +schema: reddit_ads_source

Provide a blank +schema: to write to the target_schema without any suffix.

(Optional) Step 6: Additional configurations

Expand for details

Disabling null URL filtering from URL reports

The default behavior for the ad_reporting__url_report end model is to filter out records having null URL fields, however, you are able to turn off this filter if needed. To turn off the filter, include the below in your dbt_project.yml file. This variable will affect ALL Fivetran platform packages enabled in Ad Reporting, therefore either all URL reports will have null URLs filtered, or all URL reports will have null URLs included.

vars:
  ad_reporting__url_report__using_null_filter: False # Default is True.

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:

IMPORTANT: See the Apple Store dbt_project.yml and Google Play dbt_project.yml variable declarations to see the expected names.

vars:
    <default_source_table_name>_identifier: your_table_name 

(Optional) Step 7: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core™ setup guides.

(Optional) Step 8: Use predefined Metrics

Expand for details

On top of the ad_reporting__ad_report final model, the Ad Reporting dbt package defines common Metrics, including:

• Spend
• Impressions
• Clicks
• Cost per click
• Clickthrough rate
• Bounce rate
• Count of active ads
• Average spend
• Average non-zero spend

You can find the supported dimensions and full definitions of these metrics here.

To use dbt Metrics, please refer to the dbt metrics package and install the relevant version to your project's packages.yml file.

Note: The Metrics package has stricter dbt version requirements, therefore, please take note of the correct dbt version for your desired version of dbt Metrics.

To utilize the Ad Reporting's pre-defined metrics in your code, refer to the dbt metrics package usage instructions and the example below:

select *
from {{ metrics.calculate(
        metric('clicks'),
        grain='month',
        dimensions=['platform', 
                    'campaign_id', 
                    'campaign_name'
        ],
        secondary_calculations=[
            metrics.period_over_period(comparison_strategy='difference', interval=1, alias='diff_last_mth'),
            metrics.period_over_period(comparison_strategy='ratio', interval=1, alias='ratio_last_mth')
        ]
) }}

🔍 Does this package have dependencies?

This dbt package is dependent on the following dbt packages. For more information on the below packages, refer to the dbt hub site.

If you have any of these dependent packages in your own packages.yml I highly recommend you remove them to ensure there are no package version conflicts.

packages: 
  - package: fivetran/fivetran_utils
    version: [">=0.3.0", "<0.4.0"]

  - package: dbt-labs/dbt_utils
    version: [">=0.8.0", "<0.9.0"]

  - package: calogica/dbt_expectations
    version: [">=0.5.0", "<0.6.0"]

  - package: fivetran/amazon_ads
    version: [">=0.1.0", "<0.2.0"]
  
  - package: fivetran/amazon_ads_source
    version: [">=0.1.0", "<0.2.0"]

  - package: fivetran/apple_search_ads
    version: [">=0.2.0", "<0.3.0"]

  - package: fivetran/apple_search_ads_source
    version: [">=0.2.0", "<0.3.0"]
  
  - package: fivetran/facebook_ads
    version: [">=0.6.0", "<0.7.0"]

  - package: fivetran/facebook_ads_source
    version: [">=0.6.0", "<0.7.0"]
  
  - package: fivetran/google_ads
    version: [">=0.9.0", "<0.10.0"]

  - package: fivetran/google_ads_source
    version: [">=0.9.0", "<0.10.0"]

  - package: fivetran/pinterest
    version: [">=0.7.0", "<0.8.0"]

  - package: fivetran/pinterest_source
    version: [">=0.7.0", "<0.8.0"]

  - package: fivetran/microsoft_ads
    version: [">=0.6.0", "<0.7.0"]

  - package: fivetran/microsoft_ads_source
    version: [">=0.7.0", "<0.8.0"]

  - package: fivetran/linkedin
    version: [">=0.7.0", "<0.8.0"]

  - package: fivetran/linkedin_source
    version: [">=0.7.0", "<0.8.0"]

  - package: fivetran/twitter_ads
    version: [">=0.6.0", "<0.7.0"]

  - package: fivetran/twitter_ads_source
    version: [">=0.6.0", "<0.7.0"]

  - package: fivetran/snapchat_ads
    version: [">=0.5.0", "<0.6.0"]

  - package: fivetran/snapchat_ads_source
    version: [">=0.5.0", "<0.6.0"]

  - package: fivetran/tiktok_ads
    version: [">=0.3.0", "<0.4.0"]

  - package: fivetran/tiktok_ads_source
    version: [">=0.3.0", "<0.4.0"]

  - package: fivetran/reddit_ads
    version: [">=0.1.0", "<0.2.0"]

  - package: fivetran/reddit_ads_source
    version: [">=0.1.0", "<0.2.0"]

🙌 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 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.

Opinionated Decisions

In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on a few different questions we came across during development. We've consolidated significant choices we made in the DECISIONLOG.md, and will continue to update as the package evolves. We are always open to and encourage feedback on these choices, and the package in general.

Contributions

These dbt packages are developed by a small team of analytics engineers at Fivetran. However, the packages are made better by community contributions!

We highly encourage and welcome contributions to this package. Check out this post on the best workflow for contributing to a package!

🏪 Are there any resources available?

• If you encounter any questions or want to reach out for help, please refer to 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 future dbt package to be developed, then feel free to fill out our Feedback Form.
• Have questions or want to just say hi? Book a time during our office hours here or send us an email at solutions@fivetran.com.