• Produces modeled tables that leverage Mailchimp data from Fivetran's connector in the format described by this ERD.
• 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.

Each Quickstart transformation job run materializes 33 models if all components of this data model are enabled. This count includes all staging, intermediate, and final models materialized as view, table, or incremental.

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

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

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: [">=1.1.0", "<1.2.0"] # we recommend using ranges to capture non-breaking changes automatically

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/mailchimp_source in your packages.yml since this package has been deprecated.

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

#### Option A: Single connection
By default, this package runs using your [destination](https://docs.getdbt.com/docs/running-a-dbt-project/using-the-command-line-interface/configure-your-profile) 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
vars:
  mailchimp:
    mailchimp_database: your_database_name
    mailchimp_schema: your_schema_name

If you have multiple Mailchimp connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the mailchimp_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  mailchimp:
    mailchimp_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

If you are running the package through Fivetran Transformations for dbt Core™, the below step is necessary in order to synchronize model runs with your Mailchimp connections. Alternatively, you may choose to run the package through Fivetran Quickstart, which would create separate sets of models for each Mailchimp source rather than one set of unioned models.

By default, this package defines one single-connection source, called mailchimp, which will be disabled if you are unioning multiple connections. This means that your DAG will not include your Mailchimp sources, though the package will run successfully.

To properly incorporate all of your Mailchimp connections into your project's DAG:

1. Define each of your sources in a .yml file in your project. Utilize the following template for the source-level configurations, and, most importantly, copy and paste the table and column-level definitions from the package's src_mailchimp.yml file.

# a .yml file in your root project

version: 2

sources:
  - name: <name> # ex: Should match name in mailchimp_sources
    schema: <schema_name>
    database: <database_name>
    loader: fivetran
    config:
      loaded_at_field: _fivetran_synced
      freshness: # feel free to adjust to your liking
        warn_after: {count: 72, period: hour}
        error_after: {count: 168, period: hour}

    tables: # copy and paste from mailchimp/models/staging/src_mailchimp.yml - see https://support.atlassian.com/bitbucket-cloud/docs/yaml-anchors/ for how to use anchors to only do so once

Note: If there are source tables you do not have (see Step 4), you may still include them, as long as you have set the right variables to False.

2. Set the has_defined_sources variable (scoped to the mailchimp package) to True, like such:

# dbt_project.yml
vars:
  mailchimp:
    has_defined_sources: true

Your Mailchimp connection 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
  mailchimp_using_unsubscribes: false #disable if you do not have the unsubscribe table

models:
    mailchimp:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.

vars:
    mailchimp_<default_source_table_name>_identifier: your_table_name 

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"]

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.

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!

• 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.