Amplitude dbt Package

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

Resources

Number of materialized models¹: 8
Connector documentation
- Amplitude connector documentation
- Amplitude ERD
dbt package documentation

What does this dbt package do?

This package enables you to leverage enhanced event data, view aggregated session and user metrics, and analyze daily performance metrics. It creates enriched models with metrics focused on event analysis, user behavior, and session tracking.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_amplitude

Final output tables

By default, this package materializes the following final tables:

Table	Description
amplitude__event_enhanced	Tracks individual user events with enriched event type data, device information, location details, and unnested custom properties to analyze user behavior and product interactions at the event level. Example Analytics Questions: Which event types are most frequently triggered by users across different platforms or devices? How do event patterns vary by user location (city, country) or device type (iOS vs Android)? What custom event properties correlate with higher user engagement or conversion?
amplitude__sessions	Aggregates user activity into distinct sessions with metrics on session duration, event counts, and user actions to understand engagement patterns and session quality. Example Analytics Questions: What is the average session duration and event count per session by user segment? Which sessions have the highest engagement levels based on event frequency? How do session metrics vary between new and returning users?
amplitude__user_enhanced	Provides a comprehensive view of each user with lifetime metrics including total events, sessions, and engagement patterns to understand user behavior and value. Example Analytics Questions: Which users are most engaged based on total events and session counts? What is the distribution of user engagement metrics across different user segments? How do user engagement metrics change over their lifetime?
amplitude__daily_performance	Summarizes daily event activity by event type with user and session metrics to track product usage trends and identify patterns over time. Example Analytics Questions: How are daily active users and event volumes trending by event type? Which event types show the strongest day-over-day or week-over-week growth? What days of the week have the highest event activity for key user actions?

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 Amplitude 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.

Opinionated Decisions

In creating this package, which is meant for a wide range of use cases, we had to take opinionated stances on a few different questions we came across during development. We've consolidated significant choices we made in the DECISIONLOG.md, and will continue to update as the package evolves. We are always open to and encourage feedback on these choices, and the package in general.