facebook_ads Airbyte

This package models facebook_ads data from Airbyte's connector.

Let us know which connectors you would like to see next here

Models

This package contains staging models, with the following naming conventions 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.

DBT Metrics

This package contains configurations for DBT metrics for you to get up and running quickly with standard facebook_ads metrics in your existing BI tools.

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: cerebriumAI/dbt-facebook_ads
    version: ["0.1.0"]

Configuration

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

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. Suggestions to the DBT metrics are welcome too!

Resources:

• Provide feedback on our existing dbt packages or what you'd like to see next
• Join our Slack community where we inform you about changes to packages, our roadmap as well as reach out for any help