Marketo Source dbt Package (docs)

What does this dbt package do?

Produces staging tables that leverage Marketo data from Fivetran's connector in the format described by this ERD.
- Adds descriptions to tables and columns that are synced using Fivetran
- Models staging tables, which will be used in our transform package
- 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 source and modeled Marketo data through the dbt docs site.
These tables are designed to work simultaneously with our Marketo 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 Marketo 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 will need to add the below (or a variation of the below) 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 `Marketo` transformation package)

If you are not using the Marketo 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/marketo_source
    version: [">=0.11.0", "<0.12.0"]

Step 3: Define database and schema variables

By default, this package runs using your destination and the marketo schema of your target database. If this is not where your Marketo data is (for example, if your Marketo schema is named marketo_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
  marketo_database: your_database_name
  marketo_schema: your_schema_name

Step 4: Enabling/Disabling Models

This package takes into consideration tables that may not be synced due to slowness caused by the Marketo API. By default the campaign and program models are disabled. If you sync these tables, enable the modeling done by adding the following to your dbt_project.yml file:

vars:
    marketo__enable_campaigns:   true      # Enable if Fivetran is syncing the campaign table
    marketo__enable_programs:    true      # Enable if Fivetran is syncing the program table

Alternatively, you may need to disable certain models. The below models can be disabled by adding them to your dbt_project.yml file:

vars:
    marketo__activity_delete_lead_enabled:  false     # Disable if you do not have the activity_delete_lead table

(Optional) Step 5: Additional configurations

Expand for details

Passing Through Additional Columns

This package includes all source columns defined in the macros folder. If you would like to pass through additional columns to the staging models, add the following configurations to your dbt_project.yml file. These variables allow for the pass-through fields to be aliased (alias) and casted (transform_sql) if desired, but not required. Datatype casting is configured via a sql snippet within the transform_sql key. You may add the desired sql while omitting the as field_name at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables in your root dbt_project.yml.

vars:
    marketo__activity_send_email_passthrough_columns: 
      - name: "new_custom_field"
        alias: "custom_field_name"
        transform_sql:  "cast(custom_field_name as int64)"
      - name: "a_second_field"
        transform_sql:  "cast(a_second_field as string)"
    # a similar pattern can be applied to the rest of the following variables.
    marketo__program_passthrough_columns:

Changing the Build Schema

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

models:
  marketo_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 what 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:
    marketo_<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: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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.