Pardot dbt Package

This dbt package transforms data from Fivetran's Pardot connector into analytics-ready tables.

Resources

• Number of materialized models¹: 21
• Connector documentation
  • Pardot connector documentation
  • Pardot ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog

What does this dbt package do?

This package enables you to better understand your Pardot prospects, opportunities, lists, and campaign performance. It creates enriched models with metrics focused on prospect activity and campaign effectiveness.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_pardot

Final output tables

By default, this package materializes the following final tables:

Table	Description
pardot__campaigns	Each record represents a Pardot campaign enriched with aggregated metrics including prospect counts, opportunity counts by status (won/lost), and opportunity amounts by status to measure campaign performance and revenue impact. Example Analytics Questions: Which marketing campaigns are driving the most closed deals and total revenue? What's the return on investment for each campaign when comparing spend to revenue generated? Which campaigns attract plenty of leads but struggle to convert them into won opportunities?
pardot__lists	Each record represents a Pardot list enriched with aggregated activity metrics from list members including email activity counts, visit activity counts, and timestamps of most recent activities to measure list engagement levels. Example Analytics Questions: Which email lists have the most engaged subscribers based on email opens and website visits? Do dynamic lists (automatically updated by rules) perform better than static lists in driving member engagement? Which lists are actively engaging with your content right now versus going dormant?
pardot__opportunities	Each record represents a Pardot opportunity enriched with the count of associated prospects to connect sales pipeline data with prospect relationships and track opportunity value and progression. Example Analytics Questions: Which high-value deals have the best chance of closing and involve multiple decision-makers? Does having more contacts associated with a deal increase the likelihood of winning? How long does it take to close deals from different campaigns, and at which stages do they typically get stuck?
pardot__prospects	Each record represents a Pardot prospect enriched with aggregated activity metrics including email activity counts, visit activity counts, and configurable activity type metrics to analyze prospect engagement and lead quality. Example Analytics Questions: Who are your hottest leads showing the highest engagement scores and most activity with your brand? Are unsubscribed contacts still visiting your website, indicating potential re-engagement opportunities? Which prospects are actively researching on your website but haven't engaged with emails—perfect for direct sales outreach?

¹ Each Quickstart transformation job run materializes these 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.

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Prerequisites

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

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

How do I use the dbt package?

You can either add this dbt package in the Fivetran dashboard or import it into your dbt project:

• To add the package in the Fivetran dashboard, follow our Quickstart guide.
• To add the package to your dbt project, follow the setup instructions in the dbt package's README file to use this package.

Install the package

Include the following pardot 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/pardot
    version: [">=1.3.0", "<1.4.0"]

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

Define database and schema variables

Option A: Single connection

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:
    pardot_database: your_database_name
    pardot_schema: your_schema_name

Option B: Union multiple connections

If you have multiple Pardot 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 pardot_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  pardot:
    pardot_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

Recommended: Incorporate unioned sources into DAG

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 Pardot connections. Alternatively, you may choose to run the package through Fivetran Quickstart, which would create separate sets of models for each Pardot source rather than one set of unioned models.

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

To properly incorporate all of your Pardot 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_pardot.yml file.

# a .yml file in your root project

version: 2

sources:
  - name: <name> # ex: Should match name in pardot_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 pardot/models/staging/src_pardot.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 Additional configurations), 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 pardot package) to True, like such:

# dbt_project.yml
vars:
  pardot:
    has_defined_sources: true

(Optional) Additional configurations

Expand/Collapse details

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

Additional metrics

By default, this package aggregates and joins activity data onto the prospect model for email and visit events. If you want to have aggregates for other events in the visitor_activity table, use prospect_metrics_activity_types variable to generate these aggregates. Use the type_name column value:

vars:
  pardot:
    prospect_metrics_activity_types: ["form handler","webinar"]  

Changing the Build Schema

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

models:
    pardot:
      +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.

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) 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 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. Learn how to contribute to a package in dbt's Contributing to an external dbt package article.

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.