Salesforce Marketing Cloud dbt Package (Docs)

What does this dbt package do?

This package models Salesforce Marketing Cloud data from Fivetran's connector. It uses data in the format described by this ERD.

The main focus of the package is to transform the core object tables into analytics-ready models:

• Materializes Salesforce Marketing Cloud staging tables which leverage data in the format described by this ERD. The staging tables clean, test, and prepare your Salesforce Marketing Cloud data from Fivetran's connector for analysis by doing the following:
  • Primary keys are renamed from id to <table name>_id.
  • Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
  • Provides insight into your Salesforce Marketing Cloud data across the following grains:
    • Email, send, event, link, list, and subscriber
• Generates a comprehensive data dictionary of your Salesforce Marketing Cloud data through the dbt docs site.

The following table provides a detailed list of all models materialized within this package by default.

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

How do I use the dbt package?

Step 1: Prerequisites

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

• At least one Fivetran Salesforce Marketing Cloud connector syncing data into your destination.
• A BigQuery, Snowflake, Redshift, Databricks, or PostgreSQL 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']

Database Incremental Strategies

The salesforce_marketing_cloud__events_enhanced model in this package is materialized incrementally and is configured to work with the different strategies available to each supported warehouse.

For BigQuery and Databricks All-Purpose Cluster runtime destinations, we have chosen insert_overwrite as the default strategy, which benefits from the partitioning capability.

For all other Databricks runtimes, models are materialized as tables without support for incremental runs.

For Snowflake, Redshift, and Postgres databases, we have chosen delete+insert as the default strategy.

Regardless of strategy, we recommend that users periodically run a --full-refresh to ensure a high level of data quality.

Step 2: Install the package

Include the following Salesforce Marketing Cloud 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/salesforce_marketing_cloud
    version: [">=0.2.0", "<0.3.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

Single connector

By default, this package runs using your destination and the salesforce_marketing_cloud schema. If this is not where your Salesforce Marketing Cloud data is (for example, if your Salesforce Marketing Cloud schema is named salesforce_marketing_cloud_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    salesforce_marketing_cloud_database: your_database_name
    salesforce_marketing_cloud_schema: your_schema_name

Union multiple connectors

If you have multiple Salesforce Marketing Cloud connectors in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table into the transformations. You will be able to see which source it came from in the source_relation column of each model. To use this functionality, you will need to set either the salesforce_marketing_cloud_union_schemas OR salesforce_marketing_cloud_union_databases variables (cannot do both) in your root dbt_project.yml file:

vars:
    salesforce_marketing_cloud_union_schemas: ['sfmc_usa','sfmc_canada'] # use this if the data is in different schemas/datasets of the same database/project
    salesforce_marketing_cloud_union_databases: ['sfmc_usa','sfmc_canada'] # use this if the data is in different databases/projects but uses the same schema name

NOTE: The native source.yml connection set up in the package will not function when the union schema/database feature is utilized. Although the data will be correctly combined, you will not observe the sources linked to the package models in the Directed Acyclic Graph (DAG). This happens because the package includes only one defined source.yml.

To connect your multiple schema/database sources to the package models, follow the steps outlined in the Union Data Defined Sources Configuration section of the Fivetran Utils documentation for the union_data macro. This will ensure a proper configuration and correct visualization of connections in the DAG.

Step 4: Enable/Disable Variables

By default, this package brings in data from the Salesforce Marketing Cloud link and list source tables. However, if you have disabled syncing these sources, you will need to add the following configuration to your dbt_project.yml:

vars:
    salesforce_marketing_cloud__link_enabled: false # default = true
    salesforce_marketing_cloud__list_enabled: false # default = true

(Optional) Step 5: Additional configurations

Changing the Build Schema

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

models:
  salesforce_marketing_cloud:
    +schema: my_new_schema_name # leave blank for just the target_schema
    staging:
        +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:
    salesforce_marketing_cloud_<default_source_table_name>_identifier: your_table_name 

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

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 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.