HubSpot dbt Package (Docs)

What does this dbt package do?

• Produces modeled tables that leverage HubSpot data from Fivetran's connector in the format described by this ERD.
• Enables you to better understand your HubSpot email and engagement performance. The package achieves this by performing the following:
  • Generates models for contacts, companies, and deals with enriched email and engagement metrics.
  • Provides analysis-ready event tables for email and engagement activities.
• Generates a comprehensive data dictionary of your source and modeled HubSpot data through the dbt docs site.

The following table provides a detailed list of all tables materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

Materialized Models

Each Quickstart transformation job run materializes 147 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.

How do I use the dbt package?

Step 1: Prerequisites

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

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

Databricks Dispatch Configuration

If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Database Incremental Strategies

Many of the models in this package are materialized incrementally, so we have configured our models to work with the different strategies available to each supported warehouse.

For BigQuery and Databricks All Purpose Cluster runtime destinations, we have chosen insert_overwrite as the default strategy, which benefits from the partitioning capability.

For Databricks SQL Warehouse destinations, models are materialized as tables without support for incremental runs.

For Snowflake, Redshift, and Postgres databases, we have chosen delete+insert as the default strategy.

Regardless of strategy, we recommend that users periodically run a --full-refresh to ensure a high level of data quality.

Step 2: Install the package

Include the following hubspot 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/hubspot
    version: [">=1.3.0", "<1.4.0"] # we recommend using ranges to capture non-breaking changes automatically

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

Databricks dispatch configuration

If you are using a Databricks destination with this package, you must add the following (or a variation of the following) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Step 3: Define database and schema variables

Option A: Single connection

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

vars:
    hubspot:
        hubspot_database: your_database_name
        hubspot_schema: your_schema_name

Option B: Union multiple connections

If you have multiple hubspot 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 hubspot_sources variable in your root dbt_project.yml file:

# dbt_project.yml

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

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

To properly incorporate all of your hubspot 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_hubspot.yml file.

# a .yml file in your root project

version: 2

sources:
- name: <name> # ex: Should match name in hubspot_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 hubspot/models/staging/src_hubspot.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 Step 4), 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 hubspot package) to True, like such:

# dbt_project.yml
vars:
    hubspot:
        has_defined_sources: true

Step 4: Disable/enable models and sources

When setting up your Hubspot connection in Fivetran, it is possible that not every table this package expects will be synced. This can occur because you either don't use that functionality in Hubspot or have actively decided to not sync some tables. Therefore we have added enable/disable configs in the src.yml to allow you to disable certain sources not present. Downstream models are automatically disabled as well. In order to disable the relevant functionality in the package, you will need to add the relevant variables in your root dbt_project.yml.

By default, all variables are assumed to be true, with the exception of the below. These default to false and must be explicitly enabled if needed:

• hubspot_service_enabled
• hubspot_ticket_deal_enabled
• hubspot_contact_merge_audit_enabled
• hubspot_merged_deal_enabled
• hubspot_engagement_communication_enabled

You only need to add variables for the sources that differ from their defaults. To do so, add the relevant variable configuration from below to your dbt_project.yml:

vars:
  # Marketing

  hubspot_marketing_enabled: false                        # Disables all marketing models
  hubspot_contact_enabled: false                          # Disables the contact models
  hubspot_contact_form_enabled: false                     # Disables form and contact form submission data and its relationship to contacts
  hubspot_contact_list_enabled: false                     # Disables contact list models
  hubspot_contact_list_member_enabled: false              # Disables contact list member models
  hubspot_contact_merge_audit_enabled: true               # Enables the use of the CONTACT_MERGE_AUDIT table (deprecated by Hubspot v3 API) for removing merged contacts in the final models.
                                                          # If false, contacts will still be merged using the CONTACT.property_hs_calculated_merged_vids field.
                                                          # Default = False
  hubspot_contact_property_enabled: false                 # Disables the contact property models
  hubspot_contact_property_history_enabled: false         # Disables the contact property history models
  hubspot_email_event_enabled: false                      # Disables all email_event models and functionality
  hubspot_email_event_bounce_enabled: false
  hubspot_email_event_click_enabled: false
  hubspot_email_event_deferred_enabled: false
  hubspot_email_event_delivered_enabled: false
  hubspot_email_event_dropped_enabled: false
  hubspot_email_event_forward_enabled: false
  hubspot_email_event_click_enabled: false
  hubspot_email_event_open_enabled: false
  hubspot_email_event_print_enabled: false
  hubspot_email_event_sent_enabled: false
  hubspot_email_event_spam_report_enabled: false
  hubspot_email_event_status_change_enabled: false

  # Sales

  hubspot_sales_enabled: false                            # Disables all sales models
  hubspot_company_enabled: false
  hubspot_company_property_history_enabled: false         # Disables the company property history models
  hubspot_deal_enabled: false
  hubspot_deal_company_enabled: false
  hubspot_deal_contact_enabled: false
  hubspot_deal_property_history_enabled: false            # Disables the deal property history models
  hubspot_engagement_enabled: false                       # Disables all engagement models and functionality
  hubspot_engagement_call_enabled: false
  hubspot_engagement_company_enabled: false
  hubspot_engagement_communication_enabled: true          # Enables the link between communications and engagements. Default = False
  hubspot_engagement_contact_enabled: false
  hubspot_engagement_deal_enabled: false
  hubspot_engagement_email_enabled: false
  hubspot_engagement_meeting_enabled: false
  hubspot_engagement_note_enabled: false
  hubspot_engagement_task_enabled: false
  hubspot_merged_deal_enabled: true                       # Enables the merged_deal table to filter merged deals from final models. Default = False
  hubspot_owner_enabled: false
  hubspot_property_enabled: false                         # Disables property and property_option tables
  hubspot_role_enabled: false                             # Disables role metadata
  hubspot_team_enabled: false                             # Disables team metadata
  hubspot_team_user_enabled: false                        # Disables user-to-team relationships

  # Service
  hubspot_service_enabled: true                           # Enables all service models. Default = False
  hubspot_ticket_deal_enabled: true                       # Enables ticket_deal transformations. Default = False

(Optional) Step 5: Additional configurations

Configure email metrics

This package allows you to specify which email metrics (total count and total unique count) you would like to be calculated for specified fields within the hubspot__email_campaigns model. By default, the email_metrics variable below includes all the shown fields. If you would like to remove any field metrics from the final model, you may copy and paste the below snippet within your root dbt_project.yml and remove any fields you want to be ignored in the final model.

vars:
  email_metrics: ['bounces',      #Remove if you do not want metrics in final model.
                  'clicks',       #Remove if you do not want metrics in final model.
                  'deferrals',    #Remove if you do not want metrics in final model.
                  'deliveries',   #Remove if you do not want metrics in final model.
                  'drops',        #Remove if you do not want metrics in final model.
                  'forwards',     #Remove if you do not want metrics in final model.
                  'opens',        #Remove if you do not want metrics in final model.
                  'prints',       #Remove if you do not want metrics in final model.
                  'spam_reports', #Remove if you do not want metrics in final model.
                  'unsubscribes'  #Remove if you do not want metrics in final model.
                  ]

Include passthrough columns

This package includes all source columns defined in the macros folder. We highly recommend including custom fields in this package as models now only bring in a few fields for the company, contact, deal, and ticket tables. You can add more columns using our pass-through column variables. These variables allow for the pass-through fields to be aliased (alias) and casted (transform_sql) if desired, but not required. Datatype casting is configured via a sql snippet within the transform_sql key. You may add the desired sql while omitting the as field_name at the end and your custom pass-though fields will be casted accordingly. Use the below format for declaring the respective pass-through variables in your root dbt_project.yml.

vars:
  hubspot__deal_pass_through_columns:
    - name:           "property_field_new_id"
      alias:          "new_name_for_this_field_id"
      transform_sql:  "cast(new_name_for_this_field as int64)"
    - name:           "this_other_field"
      transform_sql:  "cast(this_other_field as string)"
  hubspot__contact_pass_through_columns:
    - name:           "wow_i_can_add_all_my_custom_fields"
      alias:          "best_field"
  hubspot__company_pass_through_columns:
    - name:           "this_is_radical"
      alias:          "radical_field"
      transform_sql:  "cast(radical_field as string)"
  hubspot__ticket_pass_through_columns:
    - name:           "property_mmm"
      alias:          "mmm"
    - name:           "property_bop"
      alias:          "bop"

Alternatively, if you would like to simply pass through all columns in the above four tables, add the following configuration to your dbt_project.yml. Note that this will override any hubspot__[table_name]_pass_through_columns variables.

vars:
  hubspot__pass_through_all_columns: true # default is false

Adding property label

For property_hs_* columns, you can enable the corresponding, human-readable property_option.label to be included in the staging models.

Important

• You must have sources property and property_option enabled to enable labels. By default, these sources are enabled.
• You CANNOT enable labels if using hubspot__pass_through_all_columns: true.
• We recommend being selective with the label columns you add. As you add more label columns, your run time will increase due to the underlying logic requirements.

To enable labels for a given property, set the property attribute add_property_label: true, using the below format.

vars:
  hubspot__ticket_pass_through_columns:
    - name: "property_hs_fieldname"
      alias: "fieldname"
      add_property_label: true

Alternatively, you can enable labels for all passthrough properties by using variable hubspot__enable_all_property_labels: true, formatted like the below example.

vars:
  hubspot__enable_all_property_labels: true
  hubspot__ticket_pass_through_columns:
    - name: "property_hs_fieldname1"
    - name: "property_hs_fieldname2"

Including calculated fields

This package also provides the ability to pass calculated fields through to the company, contact, deal, and ticket staging models. If you would like to add a calculated field to any of the mentioned staging models, you may configure the respective hubspot__[table_name]_calculated_fields variables with the name of the field you would like to create, and the transform_sql which will be the actual calculation that will make up the calculated field.

vars:
  hubspot__deal_calculated_fields:
    - name:          "deal_calculated_field"
      transform_sql: "existing_field * other_field"
  hubspot__company_calculated_fields:
    - name:          "company_calculated_field"
      transform_sql: "concat(name_field, '_company_name')"
  hubspot__contact_calculated_fields:
    - name:          "contact_calculated_field"
      transform_sql: "contact_revenue - contact_expense"
  hubspot__ticket_calculated_fields:
    - name:          "ticket_calculated_field"
      transform_sql: "total_field / other_total_field"

Filtering email events

When leveraging email events, HubSpot customers may take advantage of filtering out specified email events. These filtered email events are present within the stg_hubspot__email_events model and are identified by the is_filtered_event boolean field. By default, these events are included in the staging and downstream models generated from this package. However, if you wish to remove these filtered events you may do so by setting the hubspot_using_all_email_events variable to false. See below for exact configurations you may provide in your dbt_project.yml file:

vars:
  hubspot_using_all_email_events: false # True by default

Daily ticket history

The hubspot__daily_ticket_history model is disabled by default, but will materialize if hubspot_service_enabled is set to true. See additional configurations for this model below.

Note: hubspot__daily_ticket_history and its parent intermediate models are incremental. After making any of the below configurations, you will need to run a full refresh.

Tracking ticket properties

By default, hubspot__daily_ticket_history will track each ticket's state, pipeline, and pipeline stage and pivot these properties into columns. However, any property from the source TICKET_PROPERTY_HISTORY table can be tracked and pivoted out into columns. To add other properties to this end model, add the following configuration to your dbt_project.yml file:

vars:
  hubspot__ticket_property_history_columns:
    - the
    - list
    - of 
    - property
    - names

Extending ticket history past closing date

This package will create a row in hubspot__daily_ticket_history for each day that a ticket is open, starting at its creation date. A Hubspot ticket can be altered after being closed, so its properties can change after this date.

By default, the package will track a ticket up to its closing date (or the current date if still open). To capture post-closure changes, you may want to extend a ticket's history past the close date. To do so, add the following configuration to your root dbt_project.yml file:

vars:
  hubspot:
    ticket_history_extension_days: integer_number_of_days # default = 0

Changing the Build Schema

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

models:
    hubspot:
      +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:
    hubspot_<default_source_table_name>_identifier: your_table_name

(Optional) Step 6: Orchestrate your models with Fivetran Transformations for dbt Core™

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"]

    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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. Check out this dbt Discourse article on the best workflow for contributing to a 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.