Mixpanel dbt Package

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

Resources

Number of materialized models¹: 6
Connector documentation
- Mixpanel connector documentation
- Mixpanel ERD
dbt package documentation

What does this dbt package do?

This package enables you to better understand user activity and retention through your event data, create daily and monthly timelines of events with user metrics, and aggregate events into unique user sessions. It creates enriched models with metrics focused on user activity, retention, and event frequency.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_mixpanel

Final output tables

By default, this package materializes the following final tables:

Table	Description
mixpanel__event	Tracks de-duplicated user events with default Mixpanel properties and custom event-specific attributes to analyze individual user actions and behavior patterns across your product. Example Analytics Questions: Which events are most frequently performed by users across different platforms or browsers? How do custom event properties correlate with user retention or conversion outcomes? What event sequences lead to key conversion or engagement milestones?
mixpanel__daily_events	Aggregates daily event activity with user segmentation metrics including new, repeat, and returning users, plus trailing 7-day and 28-day active user counts to track engagement trends. Example Analytics Questions: How are daily active users (DAU) and weekly active users (WAU) trending by event type? What is the ratio of new users to repeat users performing key events each day? Which events show the strongest user retention based on returning and repeat user metrics?
mixpanel__monthly_events	Summarizes monthly event activity with cohort metrics including new, repeat, returning, and churned users, plus total monthly active users (MAU) to understand long-term engagement patterns. Example Analytics Questions: How are monthly active users (MAU) trending overall and by event type? What is the monthly user churn rate and how does it vary across different events? Which events have the highest proportion of new versus repeat users month-over-month?
mixpanel__sessions	Groups user events into sessions with metrics on event frequency and action types to analyze user engagement quality and session-level behavior patterns. Example Analytics Questions: What is the average event count per session and how frequently do users create sessions? Which sessions contain specific event types based on event frequency patterns? How do session metrics vary by device and user behavior patterns?

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 Mixpanel 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.