Hubspot dbt Package

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

Resources

Number of materialized models¹: 147
Connector documentation
- Hubspot connector documentation
- Hubspot ERD
dbt package documentation

What does this dbt package do?

This package enables you to better understand your HubSpot email and engagement performance and generates comprehensive data dictionaries. It creates enriched models with metrics focused on contacts, companies, deals, and analysis-ready event tables for email and engagement activities.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_hubspot

Final output tables

By default, this package materializes the following final tables:

Table	Description
hubspot__companies	Each record represents a company in Hubspot, enriched with metrics about engagement activities. Example Analytics Questions: Which companies receive many emails but send few replies? Which industries or regions have the most engaged companies?
hubspot__company_history	Each record represents a change to a company in Hubspot, with `valid_to` and `valid_from` information. Example Analytics Questions: Which companies change owners or lifecycle stages most often? How long after updating company details do deals typically get created?
hubspot__contacts	Each record represents a contact in Hubspot, enriched with metrics about email and engagement activities. Example Analytics Questions: Which job titles have the highest email open and click rates? Do contacts prefer calls and meetings or email?
hubspot__contact_history	Each record represents a change to a contact in Hubspot, with `valid_to` and `valid_from` information. Example Analytics Questions: What is the typical progression timeline of contact lifecycle stage changes from "lead" to "customer"? What proportion of contacts revert to earlier lifecycle stages?
hubspot__contact_lists	Each record represents a contact list in Hubspot, enriched with metrics about email activities. Example Analytics Questions: Which contact lists have the highest click-to-open ratios and lowest unsubscribe rates? Which contact lists show high bounce rates or low delivery rates?
hubspot__deals	Each record represents a deal in Hubspot, enriched with metrics about engagement activities. Example Analytics Questions: How do won deals differ from lost deals in engagement activity? Which high-value deals have low engagement and may be at risk?
hubspot__deal_stages	Each record represents when a deal stage changes in Hubspot, with stage entry/exit dates and pipeline metadata. Example Analytics Questions: Which pipeline stages have the highest drop-off rates? Which deals are currently in stages longer than the historical average, indicating stalled opportunities?
hubspot__deal_history	Each record represents a change to a deal in Hubspot, with `valid_to` and `valid_from` information. Example Analytics Questions: How do deal amounts fluctuate throughout the sales cycle? Which deals have experienced frequent ownership transfers or reassignments, possibly slowing progress?
hubspot__tickets	Each record represents a ticket in Hubspot, enriched with metrics about engagement activities and information on associated deals, contacts, companies, and owners. Example Analytics Questions: Which currently open tickets are linked to high-value customers or companies and should be prioritized? Which customers generate the highest support volume relative to their deal size or lifetime value?
hubspot__daily_ticket_history	Each record represents a ticket's day in Hubspot with tracked properties pivoted out into columns. Example Analytics Questions: How long did tickets spend in each pipeline stage on average last quarter? What is the distribution of ticket ages by priority level and pipeline stage?
hubspot__email_campaigns	Each record represents a email campaign in Hubspot, enriched with metrics about email activities. Example Analytics Questions: What is the click-to-open ratio by campaign type (newsletter vs. promotional vs. nurture)? What is the relationship between a campaign's number of messages and recipient engagement?
hubspot__email_event_*	Each record represents an email event in Hubspot, joined with relevant tables to make them analysis-ready. Example Analytics Questions: How do spam reports vary by sender domain or audience source? Which links or CTAs receive the most clicks?
hubspot__email_sends	Each record represents a sent email in Hubspot, enriched with metrics about opens, clicks, and other email activity. Example Analytics Questions: Which recipient domains have the highest bounce rates? What are the optimal send timing patterns based on open and click performance across different contact segments?
hubspot__engagements	Each record represents an engagement in Hubspot, enriched with contact, company, and deal information. Example Analytics Questions: Which sales reps log the highest number of engagements overall, and what is their activity mix (calls vs. emails vs. meetings)? What is the average time between customer-initiated engagement (e.g., inbound email) and follow-up activity from reps?
hubspot__engagement_*	Each record represents an engagement event in Hubspot, joined with relevant tables to make them analysis-ready. Example Analytics Questions: What are the busiest call days and times for successful connections? Which reps consistently schedule follow-up meetings after initial contact?

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.

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.

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.

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.