Microsoft Ads Transformation dbt Package (Docs)
- Produces modeled tables that leverage Microsoft Ads data from Fivetran's connector in the format described by this ERD and builds off the output of our Microsoft Ads source package.
- Enables you to better understand the performance of your ads across varying grains:
- Providing an account, campaign, ad group, keyword, ad, utm and search level reports.
- Materializes output models designed to work simultaneously with our multi-platform Ad Reporting package.
- Generates a comprehensive data dictionary of your source and modeled Microsoft Ads data through the dbt docs site.
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 |
|---|---|
| microsoft_ads__account_report | Each record in this table represents the daily performance at the account level. |
| microsoft_ads__campaign_report | Each record in this table represents the daily performance of a campaign at the campaign/advertising_channel/advertising_channel_subtype level. |
| microsoft_ads__ad_group_report | Each record in this table represents the daily performance at the ad group level. |
| microsoft_ads__ad_report | Each record in this table represents the daily performance at the ad level. |
| microsoft_ads__keyword_report | Each record in this table represents the daily performance at the ad group level for keywords. |
| microsoft_ads__search_report | Each record in this table represents the daily performance at the search level. |
| microsoft_ads__url_report | Each record in this table represents the daily performance of URLs at the ad level. |
To use this dbt package, you must have the following:
- At least one Fivetran Microsoft Ads connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
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 microsoft_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:
- package: fivetran/microsoft_ads
version: [">=0.5.0", "<0.6.0"]By default, this package runs using your destination and the microsoft_ads schema. If this is not where your Microsoft Ads data is (for example, if your Microsoft Ads schema is named microsoft_ads_fivetran), add the following configuration to your root dbt_project.yml file:
vars:
microsoft_ads_database: your_destination_name
microsoft_ads_schema: your_schema_name Expand for configurations
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.
vars:
microsoft_ads__account_passthrough_metrics:
- name: "new_custom_field"
alias: "custom_field"
microsoft_ads__campaign_passthrough_metrics:
- name: "this_field"
microsoft_ads__ad_group_passthrough_metrics:
- name: "unique_string_field"
alias: "field_id"
microsoft_ads__ad_passthrough_metrics:
- name: "new_custom_field"
alias: "custom_field"
- name: "a_second_field"
microsoft_ads__keyword_passthrough_metrics:
- name: "this_field"
microsoft_ads__search_passthrough_metrics:
- name: "unique_string_field"
alias: "field_id"This package assumes you are manually adding UTM tags to your ads. If you are leveraging the auto-tag feature within Microsoft Ads then you will want to enable the google_auto_tagging_enabled variable to correctly populate the UTM fields within the microsoft_ads__utm_report model.
vars:
microsoft_ads_auto_tagging_enabled: true # False by defaultBy default, this package builds the Microsoft Ads staging models within a schema titled (<target_schema> _microsoft_ads_source) and your Microsoft Ads modeling models within a schema titled (<target_schema> _microsoft_ads) in your destination. If this is not where you would like your Microsoft Ads data to be written to, add the following configuration to your root dbt_project.yml file:
models:
microsoft_ads_source:
schema: my_new_schema_name # leave blank for just the target_schema
microsoft_ads:
schema: my_new_schema_name # leave blank for just the target_schemaIf 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.ymlvariable declarations to see the expected names.
vars:
microsoft_ads_<default_source_table_name>_identifier: your_table_name Expand for more 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.
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.ymlfile, we highly recommend that you remove them from your rootpackages.ymlto avoid package version conflicts.
packages:
- package: fivetran/microsoft_ads_source
version: [">=0.8.0", "<0.9.0"]
- package: fivetran/fivetran_utils
version: [">=0.3.0", "<0.4.0"]
- package: dbt-labs/dbt_utils
version: [">=0.8.0", "<0.9.0"]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.
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.
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!
- If you have 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 new dbt package, fill out our Feedback Form.
- Have questions or want to just say hi? Book a time during our office hours on Calendly or email us at [email protected].
