Pardot Source dbt Package (Docs)

What does this dbt package do?

Materializes Pardot staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your Pardot 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 Pardot data through the dbt docs site.
These tables are designed to work simultaneously with our Pardot 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 Pardot connector syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

Step 2: Install the package (skip if also using the `pardot` transformation package)

If you are not using the Pardot 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/pardot_source
    version: [">=0.6.0", "<0.7.0"]

Step 3: Define database and schema variables

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

vars:
  pardot_source:
    pardot_database: your_database_name
    pardot_schema: your_schema_name

(Optional) Step 4: Additional configurations

Expand to view configurations

Changing the Build Schema

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

models:
  pardot_source:
    +schema: my_new_staging_models_schema # leave blank for just the target_schema

Passthrough Columns

By default, the package includes all of the standard columns in the stg_pardot__prospect model. If you want to include custom columns, configure them using the prospect_passthrough_columns variable:

vars:
  pardot_source:
    prospect_passthrough_columns: ["custom_creative","custom_contact_state"]

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:
    pardot_<default_source_table_name>_identifier: your_table_name

(Optional) Step 5: 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.