Pardot (docs)
This package models Pardot data from Fivetran's connector. It uses data in the format described by the Pardot ERD.
This package enables you to better understand your Pardot prospects, opportunities, lists, and campaign performance. It includes analysis-ready models, enriched with relevant metrics.
This package contains transformation models, designed to work simultaneously with our Pardot source package. A dependency on the source package is declared in this package's packages.yml file, so it will automatically download when you run dbt deps. The primary outputs of this package are described below. Intermediate models are used to create these output models.
| model | description |
|---|---|
| pardot__campaigns | Each record represents a campaign in Pardot, enriched with metrics about associated prospects. |
| pardot__lists | Each record represents a list in Pardot, enriched with metrics about associated prospect activity. |
| pardot__opportunities | Each record represents an opportunity in Pardot, enriched with metrics about associated prospects. |
| pardot__prospects | Each record represents a prospect in Pardot, enriched with metrics about associated prospect activity. |
Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.
Include in your packages.yml
packages:
- package: fivetran/pardot
version: [">=0.4.0", "<0.5.0"]By default, this package will look for your Pardot data in the pardot schema of your target database. If this is not where your Pardot data is, add the following configuration to your dbt_project.yml file:
# dbt_project.yml
...
config-version: 2
vars:
pardot_source:
pardot_database: your_database_name
pardot_schema: your_schema_name 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:
# dbt_project.yml
...
vars:
pardot_source:
prospect_passthrough_columns: ["custom_creative","custom_contact_state"]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:
# dbt_project.yml
...
vars:
pardot:
prospect_metrics_activity_types: ["form handler","webinar"] 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 to, add the following configuration to your dbt_project.yml file:
# dbt_project.yml
...
models:
pardot:
+schema: my_new_schema_name # leave blank for just the target_schema
pardot_source:
+schema: my_new_schema_name # leave blank for just the target_schemaThis package has been tested on BigQuery, Snowflake, Redshift, and Postgres.
Additional contributions to this package are very welcome! Please create issues
or open PRs against master. Check out
this post
on the best workflow for contributing to a package.
- Provide feedback on our existing dbt packages or what you'd like to see next
- Have questions, feedback, or need help? Book a time during our office hours using Calendly or email us at [email protected]
- Find all of Fivetran's pre-built dbt packages in our dbt hub
- Learn how to orchestrate dbt transformations with Fivetran
- Learn more about Fivetran overall in our docs
- Check out Fivetran's blog
- Learn more about dbt in the dbt docs
- Check out Discourse for commonly asked questions and answers
- Join the chat on Slack for live discussions and support
- Find dbt events near you
- Check out the dbt blog for the latest news on dbt's development and best practices