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Greenhouse Source dbt Package (Docs)

What does this dbt package do?

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

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

Include the following greenhouse_source 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/greenhouse_source
    version: [">=0.8.0", "<0.9.0"] # we recommend using ranges to capture non-breaking changes automatically

Step 3: Define database and schema variables

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

vars:
    greenhouse_database: your_database_name
    greenhouse_schema: your_schema_name

Step 4: Disable models for non-existent sources

Your Greenhouse connector might not sync every table that this package expects. If your syncs exclude certain tables, it is because you either do not use that functionality in Greenhouse or have actively excluded some tables from your syncs.

To disable the corresponding functionality in the package, you must set the relevant config variables to false. By default, all variables are set to true. Alter variables only for the tables you want to disable:

vars:
    greenhouse_using_prospects: false # Disable if you do not use prospects and/or do not have the PROPECT_POOL and PROSPECT_STAGE tables synced
    greenhouse_using_eeoc: false # Disable if you do not have EEOC data synced and/or do not want to integrate it into the package models
    greenhouse_using_app_history: false # Disable if you do not have APPLICATION_HISTORY synced and/or do not want to run the application_history transform model
    greenhouse_using_job_office: false # Disable if you do not have JOB_OFFICE and/or OFFICE synced, or do not want to include offices in the job_enhanced transform model
    greenhouse_using_job_department: false # Disable if you do not have JOB_DEPARTMENT and/or DEPARTMENT synced, or do not want to include offices in the job_enhanced transform model

(Optional) Step 5: Additional configurations

Expand to view configurations

Passthrough Custom Columns

The Greenhouse APPLICATION, JOB, and CANDIDATE tables may have custom columns, all prefixed with custom_field_. To pass these columns along to the staging and final transformation models, add the following variables to your dbt_project.yml file:

vars:
    greenhouse_application_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final application_enhanced model
    greenhouse_candidate_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final application_enhanced model
    greenhouse_job_custom_columns: ['the', 'list', 'of', 'columns'] # these columns will be in the final job_enhanced model

Changing the Build Schema

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

models:
    greenhouse_source:
        +schema: my_new_staging_models_schema # leave blank for just the 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:
    greenhouse_<default_source_table_name>_identifier: your_table_name

(Optional) Step 6: 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.