Updated November 1, 2023
📣 What does this dbt package do?link
- Produces modeled tables that leverage Facebook Ads data from Fivetran's connector in the format described by this ERD and builds off the output of our Facebook Ads source package.
- Enables you to better understand the performance of your ads across varying grains:
- Providing an account, campaign, ad group, keyword, ad, and utm 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 Facebook 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.
|facebook_ads__account_report||Each record in this table represents the daily performance at the account level.|
|facebook_ads__campaign_report||Each record in this table represents the daily performance of a campaign at the campaign/advertising_channel/advertising_channel_subtype level.|
|facebook_ads__ad_set_report||Each record in this table represents the daily performance at the ad set level.|
|facebook_ads__ad_report||Each record in this table represents the daily performance at the ad level.|
|facebook_ads__utm_report||Each record in this table represents the daily performance of URLs at the ad level.|
🎯 How do I use the dbt package?link
Step 1: Prerequisiteslink
To use this dbt package, you must have the following:
- At least one Fivetran Facebook Ads connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
- You will need to configure your Facebook Ads connector to pull the
basic_adpre-built report. This pre-built report should be enabled in your connector by default. However, to confirm this pre-built report is actively syncing you may perform the following steps:
- Navigate to the connector schema tab.
- Search for
basic_adand confirm it is selected.
- If not selected, do so and sync. If already selected you are ready to run the models!
Databricks Dispatch Configurationlink
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']content_copy
Step 2: Install the packagelink
Include the following facebook_ads package version in your
packages: - package: fivetran/facebook_ads version: [">=0.7.0", "<0.8.0"] # we recommend using ranges to capture non-breaking changes automaticallycontent_copy
Do NOT include the
facebook_ads_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.
Step 3: Define database and schema variableslink
By default, this package runs using your destination and the
facebook_ads schema. If this is not where your Facebook Ads data is (for example, if your Facebook Ads schema is named
facebook_ads_fivetran), add the following configuration to your root
vars: facebook_ads_database: your_destination_name facebook_ads_schema: your_schema_namecontent_copy
(Optional) Step 4: Additional configurationslink
Union multiple connectorslink
If you have multiple facebook_ads 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
facebook_ads_union_databases variables (cannot do both) in your root
vars: facebook_ads_union_schemas: ['facebook_ads_usa','facebook_ads_canada'] # use this if the data is in different schemas/datasets of the same database/project facebook_ads_union_databases: ['facebook_ads_usa','facebook_ads_canada'] # use this if the data is in different databases/projects but uses the same schema namecontent_copy
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
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.
Passing Through Additional Metricslink
By default, this package will select
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: facebook_ads__basic_ad_passthrough_metrics: - name: "new_custom_field" alias: "custom_field" - name: "another_one"content_copy
Change the build schemalink
By default, this package builds the Facebook Ads staging models within a schema titled (
_facebook_ads_source) and your Facebook Ads modeling models within a schema titled (
_facebook_ads) in your destination. If this is not where you would like your Facebook Ads data to be written to, add the following configuration to your root
models: facebook_ads_source: +schema: my_new_schema_name # leave blank for just the target_schema facebook_ads: +schema: my_new_schema_name # leave blank for just the target_schemacontent_copy
Change the source table referenceslink
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.ymlvariable declarations to see the expected names.
vars: facebook_ads_<default_source_table_name>_identifier: your_table_namecontent_copy
(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™link
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.
🔍 Does this package have dependencies?link
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 root
packages.ymlto avoid package version conflicts.
packages: - package: fivetran/facebook_ads_source version: [">=0.7.0", "<0.8.0"] - 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"]content_copy
🙌 How is this package maintained and can I contribute?link
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, DECISIONLOG 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!
We highly encourage and welcome contributions to this package. Check out this dbt Discourse article on the best workflow for contributing to a package!
🏪 Are there any resources available?link
- 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 be part of the community discourse? Create a post in the Fivetran community and our team along with the community can join in on the discussion!