Facebook Ads (Source)

This package models Facebook Ads data from Fivetran's connector. It uses data in the format described by this ERD.

Models

This package contains staging models, designed to work simultaneously with our Facebook Ads modeling package and our multi-platform Ad Reporting package. The staging models name columns consistently across all packages:

• Boolean fields are prefixed with is_ or has_
• Timestamps are appended with _timestamp
• ID primary keys are prefixed with the name of the table. For example, the campaign table's ID column is renamed campaign_id.

Installation Instructions

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

Include in your packages.yml

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

Configuration

Required Report(s)

To use this package, you will need to configure your Facebook Ads connector to pull the BASIC_AD pre-built report. Follow the below steps in the Fivetran UI to do so:

1. Navigate to the connector setup form (Setup -> Edit connection details for pre-existing connectors)
2. Click Add table
3. Select Pre-built Report
4. Set the table name to basic_ad
5. Select BASIC_AD as the corresponding pre-built report
6. Select a daily aggregation period
7. Click Ok and Save & test!

Source Data Location

By default, this package will look for your Facebook Ads data in the facebook_ads schema of your target database. If this is not where your Facebook Ads data is, please add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
    facebook_ads_schema: your_schema_name
    facebook_ads_database: your_database_name 

Changing the Build Schema

By default this package will build the Facebook Ads staging models within a schema titled (<target_schema> + _stg_facebook_ads) in your target database. If this is not where you would like your Facebook Ads staging data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
models:
    facebook_ads_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

Database Support

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.

Databricks Dispatch Configuration

dbt v0.20.0 introduced a new project-level dispatch configuration that enables an "override" setting for all dispatched macros. 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.

# dbt_project.yml

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

Contributions

Additional contributions to this package are very welcome! Please create issues or open PRs against master. Check out this post on the best workflow for contributing to a package.

Resources:

• Provide feedback on our existing dbt packages or what you'd like to see next
• Have questions, feedback, or need help? Book a time during our office hours using Calendly or email us at solutions@fivetran.com
• Find all of Fivetran's pre-built dbt packages in our dbt hub
• Learn how to orchestrate dbt transformations with Fivetran
• Learn more about Fivetran overall in our docs
• Check out Fivetran's blog
• Learn more about dbt in the dbt docs
• Check out Discourse for commonly asked questions and answers
• Join the chat on Slack for live discussions and support
• Find dbt events near you
• Check out the dbt blog for the latest news on dbt's development and best practices