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Mailchimp Source dbt Package (Docs)

What does this dbt package do?

  • Materializes Mailchimp staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Mailchimp data from Fivetran's connector for analysis by doing the following:
    • Name columns for consistency across all packages and for easier analysis
    • Adds freshness tests to source data
    • Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
  • Generates a comprehensive data dictionary of your Mailchimp data through the dbt docs site.
  • These tables are designed to work simultaneously with our Mailchimp transformation package.

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.

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 2: Install the package (skip if also using the mailchimp transformation package)

If you are NOT using the Mailchimp transformation package, include the following package version in your packages.yml file. If you are installing the transform package, the source package is automatically installed as a dependency.

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

packages:
  - package: fivetran/mailchimp_source
    version: [">=0.5.0", "<0.6.0"] # we recommend using ranges to capture non-breaking changes automatically

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:

vars:
    mailchimp_database: your_database_name
    mailchimp_schema: your_schema_name

Step 4: Disable models for non-existent sources

Your Mailchimp connector may not sync every table that this package expects. If your syncs exclude certain tables, it is because you either do not use that functionality in Mailchimp or 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 or segment_member table

(Optional) Step 5: Additional configurations

Expand to view configurations

Changing the Build Schema

By default, this package builds the Mailchimp staging models within a schema titled (<target_schema> + _stg_mailchimp) in your destination. If this is not where you would like your Mailchimp staging data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    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

Passthrough Columns

This package includes all of the source columns that are defined in the macros folder. We recommend including custom columns in this package because the staging models only bring in the standard columns for the MEMBER table.

You can add more columns using our passthrough column variables. These variables allow the passthrough columns to be aliased (alias) and casted (transform_sql) if you want, although it is not required. You can configure datatype casting by using a SQL snippet within the transform_sql key. You may add the desired SQL snippet while omitting the as field_name part of the casting statement - we rename this column with the alias attribute - and your custom passthrough columns will be casted accordingly.

Use the following format for declaring the respective passthrough variables (note to call the alias name, not original name, in the transform_sql):

vars:
  mailchimp__members_pass_through_columns:
    - name:           "custom_field_name"
      alias:          "normal_field_name"
      transform_sql:  "cast(normal_field_name as string)"

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

Expand to view 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"]

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 that 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 to learn how to contribute to a dbt 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.