StackAdapt dbt Package

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

Resources

Number of materialized models¹: 21
Connector documentation
- StackAdapt connector documentation
- StackAdapt ERD
dbt package documentation
dbt Core™ supported versions
- >=1.3.0, <3.0.0

What does this dbt package do?

This package enables you to better understand the performance of your ads across varying grains and produces modeled tables that leverage StackAdapt data. It creates enriched models with metrics focused on advertiser, campaign group, campaign, and ad level reports.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_stackadapt_reports

Final output tables

By default, this package materializes the following final tables:

Table	Description
`stackadapt__advertiser_report`	Represents daily performance aggregated at the advertiser level, including `impressions`, `clicks`, `spend`, and `conversions`. Example Analytics Questions: How does performance compare across different advertisers? Which advertisers are driving the most spend or conversions?
`stackadapt__campaign_group_report`	Represents daily performance aggregated at the campaign group level, including `impressions`, `clicks`, `spend`, and `conversions`. Example Analytics Questions: Which campaign groups have the highest click-through rate or conversion rate? How does spend and performance compare across campaign groups within each advertiser?
`stackadapt__campaign_report`	Represents daily performance aggregated at the campaign level, including `impressions`, `clicks`, `spend`, and `conversions`. Example Analytics Questions: Which campaigns are most efficient in terms of cost per conversion? How does delivery performance vary by campaign channel type and status?
`stackadapt__ad_report`	Represents daily performance at the individual ad level, including `impressions`, `clicks`, `spend`, and `conversions`. Example Analytics Questions: Which ad creatives are driving the lowest cost per click? How do different ad sizes compare in performance? Which ads are top-performing within each campaign?
`stackadapt__url_report`	Represents daily performance aggregated at the landing URL level, with URL components and UTM parameters parsed from `click_url`. Includes `impressions`, `clicks`, `spend`, and `conversions`. Example Analytics Questions: Which landing pages are driving the most conversions? How does performance compare across UTM campaigns and sources? Which URL paths have the highest spend relative to conversions?

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 StackAdapt connection created on or after January 15, 2026 using the StackAdapt GraphQL API 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.