Mailchimp Transformation dbt Package (Docs)

What does this dbt package do?

Produces modeled tables that leverage Mailchimp data from Fivetran's connector in the format described by this ERD and builds off the output of our Mailchimp source package.
Transforms the 'recipient' and 'activity' tables into analytics-ready models and use that data to provide aggregate metrics about campaigns, automations, lists, members, and segments.

Generates a comprehensive data dictionary of your source and modeled Mailchimp data through the dbt docs site. The following table provides a detailed list of all tables materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

Table	Description
mailchimp__automations_activities	Each record represents an activity taken in relation to a automation email.
mailchimp__automation_emails	Each record represents an automation email (that make up automations), enriched with click, open, and unsubscribe metrics. This output is enabled if you are using automations.
mailchimp__automations	Each record represents an automation in Mailchimp, enriched with click, open, and unsubscribe metrics. This output is enabled if you are using automations.
mailchimp__campaign_activities	Each record represents an activity taken in relation to a campaign email, enriched with data about when the campaign was sent and the lag between send and the activity.
mailchimp__campaign_recipients	Each record represents the send of a campaign email, enriched with click, open, and unsubscribe metrics.
mailchimp__campaigns	Each record represents a campaign in Mailchimp, enriched with click, open, and unsubscribe metrics.
mailchimp__lists	Each record represents a list in Mailchimp, enriched with campaign metrics, (optional) automation metrics, and (optional) information about members.
mailchimp__members	Each record represents a member in Mailchimp, enriched with campaign metrics and (optional) automation metrics.
mailchimp__segments	Each record represents a segment in Mailchimp, enriched with campaign metrics and (optional) automation metrics. This output is enabled if you are using segments.

How do I use the dbt package?

Step 1: Prerequisites

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

At least one Fivetran Mailchimp connector syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Step 2: Install the package

Include the following mailchimp 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/mailchimp
    version: [">=0.9.0", "<0.10.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the mailchimp_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following (or a variation of the following) 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']

## Step 3: Define database and schema variables
By default, this package runs using your destination and the `mailchimp` schema. If this is not where your Mailchimp data is (for example, if your Mailchimp schema is named `mailchimp_fivetran`), add the following configuration to your root `dbt_project.yml` file:

```yml {% process=false %}
vars:
    mailchimp_schema: your_schema_name
    mailchimp_database: your_database_name

Step 4: Disable models for non-existent sources

Your Mailchimp connector might not sync every table that this package expects. If your syncs exclude certain tables, it is because you either don't use that functionality in Mailchimp or have actively excluded some tables from your syncs. To disable the corresponding functionality in the package, you must set the relevant config variables to false. By default, all variables are set to true. Alter variables for only the tables you want to disable:

vars:
  mailchimp_using_automations: false # disable if you do not have the automation_email, automation_email, or automation_recipient_activity tables
  mailchimp_using_segments: false # disable if you do not have the segment table

(Optional) Step 5: Additional configurations

Expand for configurations

Changing the Build Schema

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

models:
  mailchimp:
    +schema: my_new_schema_name # leave blank for just the target_schema
  mailchimp_source:
    +schema: my_new_schema_name # leave blank for just the target_schema

Change the source table references

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.yml variable declarations to see the expected names.

vars:
    mailchimp_<default_source_table_name>_identifier: your_table_name

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for 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?

This dbt package is dependent on the following dbt packages. 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 highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]

    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]

    - package: fivetran/mailchip_source
      version: [">=0.5.0", "<0.6.0"]

🙌 How is this package maintained and can I contribute?

Package Maintenance

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.

Contributions

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?

If you have questions or want to reach out for help, see 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.