Marketo (docs)

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

This package enables you to better understand your Marketo email performance and how your leads change over time. The output includes models with enriched email metrics for leads, programs, email templates, and campaigns. It also includes a lead history table that shows the state of leads on every day, for a set of columns that you define.

Models

This package contains transformation models, designed to work simultaneously with our Marketo 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
marketo__campaigns	Each record represents a Marketo campaign, enriched with metrics about email performance.
marketo__email_sends	Each record represents the send of a Marketo email, enriched with metrics about email performance.
marketo__email_templates	Each record represents a Marketo email template, enriched with metrics about email performance.
marketo__lead_history	Each record represents the state of a lead on a specific day. The columns in this model are specified with the lead_history_columns variable.
marketo__leads	Each record represents a Marketo lead, enriched with metrics about email performance.
marketo__programs	Each record represents a Marketo program, enriched with metrics about email performance.

Installation Instructions

Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.

Configuration

By default, this package will look for your Marketo data in the marketo schema of your target database. If this is not where your Marketo data is , please add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
config-version: 2

vars:
  marketo_source:
    marketo_database: your_database_name
    marketo_schema: your_schema_name 

For additional configurations for the source models, please visit the Marketo source package.

Tracking Different Lead History Columns

The marketo__lead_history model generates historical data for the columns specified by the lead_history_columns variable. By default, the columns tracked are lead_status, urgency, priority, relative_score, relative_urgency, demographic_score_marketing, and behavior_score_marketing. If you would like to change these columns, add the following configuration to your dbt_project.yml file. After adding the columns to your dbt_project.yml file, run the dbt run --full-refresh command to fully refresh any existing models.

# dbt_project.yml

...
config-version: 2

vars:
  marketo:
    lead_history_columns: ['the','list','of','column','names']

Changing the Build Schema

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

# dbt_project.yml

...
models:
    marketo:
      +schema: my_new_schema_name # leave blank for just the target_schema
    marketo_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

Enabling 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 related-models are disabled. If you sync these tables, enable the modeling done by adding the following to your dbt_project.yml file:

# dbt_project.yml

...
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

Contributions

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.

Database Support

This package has been tested on BigQuery, Snowflake and Redshift.

Resources:

• Provide feedback on our existing dbt packages or what you'd like to see next
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• 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
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• Check out the dbt blog for the latest news on dbt's development and best practices