• Adds a number of macros that are useful when transforming data to be synced via reverse ETL (with Census or your own pipelines).
• Adds documentation for the macros at: dbt docs site.

To use this dbt package, you must have the following:

• A BigQuery, Snowflake, or Redshift data warehouse.

Include the following census_utils package version in your packages.yml file, then run 'dbt deps':

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

packages:
  - package: sutrolabs/census_utils
    version: [">=0.1.0", "<1.0.0"]

This package uses seeds for macros such as converting country codes to country names. Run 'dbt seed' after 'dbt deps' to materialize these seeds in your data warehouse.

The is_internal macro (source) identifies internal users based off of several potential methods: their email address domain, an existing list of email addresses, or an existing list of IP Addresses. If you want to use the is_internal macro, you'll need to specify at least one of these approaches. Add variables to your root dbt_project.yml file to reflect the domain of your company and the relations and columns where internal users are tracked. These relations can be a dbt seed or a dbt model. Common methods of maintaining this relation include: a seed file of internal users and IP address, a dbt model that identifies internal users directly from your application database, or a dbt model referencing a Google Sheet of internal users. For example, if your company used the domains sawtelleanalytics.com and sawtelleanalytics.co.uk, and you have a dbt seed called 'my_internal_users' with an email_address column for the emails of internal users and an ip_address column for the IPs of internal users, you would add this to your vars:

vars:
  internal_domain: ('sawtelleanalytics.com', 'sawtelleanalytics.co.uk')
  internal_email_relation: 'my_internal_users'
  internal_email_column: 'email_address'
  internal_ip_relation: 'my_internal_users'
  internal_ip_column: 'ip_address'

• parse_ga4_client_id (source)
• clean (source)
• is_internal (source)
• extract_email_domain (source)
• is_personal_email (source)
• get_country_code (source)

This macro takes a Google Analytics 4 client ID and returns either the unique ID part, the timestamp part, or the entire ID without any 'GA1.2' type prefix before it.

Args:

• client_id (required): The raw GA 4 client ID to parse.
• extract_value (required): Specifies what to extract from the raw client ID. Should be either 'unique_id', 'timestamp', or 'client_id'.

Usage:

select 
    ga4_client_id,
    {{ census_utils.parse_ga4_client_id('ga4_client_id', 'unique_id') }} as unique_id,
    {{ census_utils.parse_ga4_client_id('ga4_client_id', 'timestamp') }} as timestamp,
    {{ census_utils.parse_ga4_client_id('ga4_client_id', 'client_id') }} as client_id
from ga4_client

This macro cleans fields so that they will be accepted by APIs such as Facebook Ads or Google Ads.

Args:

• field (required): The name to be cleaned.
• destination (required): The destination the field will be sent to. Currently supports 'facebook' or 'google'.
• type (required): The type of field, currently supports 'name' (for first or last name), 'email', 'city', 'country', or 'zip'. Non-US postal codes should not be formatted by this macro as requirements for those vary.

Usage:

select 
    {{ census_utils.clean('fn',destination='facebook',type='name') }} as cleaned_facebook_fn,
    {{ census_utils.clean('ln',destination='facebook',type='name') }} as cleaned_facebook_ln,
    {{ census_utils.clean('country',destination='facebook',type='country') }} as cleaned_facebook_country,
    {{ census_utils.clean('city',destination='facebook',type='city') }} as cleaned_facebook_city,
    case when {{ census_utils.clean('country_to_clean','facebook','country') }} = 'us' then {{ census_utils.clean('zip','facebook','zip') }} else zip end as cleaned_facebook_zip,
    {{ census_utils.clean('email_address','facebook','email') }} as cleaned_facebook_email

This macro reports whether a user is an internal user based on their email domain, email address, or IP address. Relies on at least one variable being set in dbt_project.yml.

Args:

• email (optional): The email address of the user.
• ip_address (optional): The IP address of the user.

Usage:

select 
    email_address
    , ip_address
    , {{ census_utils.is_internal(email='email_address',ip_address='ip_address') }} as is_internal_user
    , {{ census_utils.is_internal(email='email_address') }} as is_internal_email
    , {{ census_utils.is_internal(ip_address='ip_address') }} as is_internal_ip
from
    users

This macro extracts the domain from an email address.

Args:

• email (required): The email address of the user.

Usage:

select 
    email_address,
    {{ census_utils.extract_email_domain('email_addresses') }} as email_domain

This macro determines whether an email address is personal, based on a list of common personal email domains.

Args:

• email (required): The email address of the user.

Usage:

select 
    email_address,
    {{ census_utils.is_personal_email('email_addresses') }} as is_personal_email

This macro converts a country name to a ISO 3166 country code. Args:

• country_name (required): The country name to be converted to a code.

Usage:

select 
    country_name,
    {{ census_utils.get_country_code('country_name') }} as country_code

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.yml file, we recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: dbt-labs/dbt_utils
      version: [">=.9.0", "<2.0.0"]

The Census 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.

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 Census or would like to request a new dbt package, please join the Operational Analytics Slack.