MNTN dbt Package

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

Resources

Number of materialized models¹: 32
Connector documentation
- MNTN connector documentation
- MNTN 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 MNTN connected TV and streaming ads across varying grains and produces modeled tables that leverage MNTN data. It creates enriched models with metrics focused on account, campaign, ad group, ad, geographic (country/state), destination URL, and audience-segment reporting.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_mntn

Final output tables

By default, this package materializes the following final tables:

Table	Description
`mntn__account_report`	Daily account-level performance, including `impressions`, `visits`, `spend`, and `conversions`. Example Analytics Questions: How does performance compare across different MNTN accounts? Is spend trending up or down over time at the account level?
`mntn__campaign_report`	Daily campaign-level performance, sourced from MNTN's `campaign_group` table (the platform's preferred campaign-level reporting grain). Example Analytics Questions: Which campaigns are most efficient in terms of cost per conversion? Which campaigns contribute most to overall spend or conversions?
`mntn__ad_group_report`	Daily ad-group-level performance, sourced from MNTN's `creative_group` table (one TV commercial plus its tracking). Example Analytics Questions: Which ad groups have the strongest engagement relative to spend? Do certain ad groups dominate impressions within a campaign?
`mntn__ad_report`	Daily ad-level performance, sourced from MNTN's `creative` table (the platform's most granular metrics grain), enriched with ad activation status and ad-serving tag attributes. Example Analytics Questions: Which ad creatives are driving the lowest cost per conversion? How do performance trends change after refreshing creative?
`mntn__country_report`	Daily performance broken out by country. Example Analytics Questions: Which countries are delivering the highest return on ad spend? Are there seasonal performance variations by geographic region?
`mntn__region_report`	Daily performance broken out by state/province. Example Analytics Questions: Which states are driving the most efficient performance? How do regional performance trends correlate with local market conditions?
`mntn__url_report`	Daily performance broken out by destination URL, parsed from `creative_info.click_url`. By default, excludes ads with NULL `click_url` values. Example Analytics Questions: Which landing pages are driving the highest conversion rates? Which UTM campaigns are driving the most traffic across different creatives?
`mntn__segment_report`	Daily audience/targeting-segment performance, MNTN's audience concept with no equivalent report type in other ad_reporting platforms. Example Analytics Questions: Which audience segments are driving the most site visits? How does new vs. existing audience reach vary by segment?

¹ Each Quickstart transformation job run materializes these models if all components of this data model are enabled. This count includes all staging and final models materialized as view or table.

Prerequisites

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

At least one Fivetran MNTN connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
The advertiser and advertiser_info source tables syncing, which power mntn__account_report. Note that account-level context (account_id/account_name) is not enriched into the other end models, since MNTN's other source tables carry no advertiser identifier of their own and a single MNTN connection can span multiple advertiser accounts.
Note that creative_group, campaign_info, creative, creative_info, ad_info, analytics_by_country, and analytics_by_state are optional source tables. If any of these are not syncing for your MNTN connection, the corresponding report(s) are disabled by default — see Enable or Disable Optional Reports below.

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.

Contributors

We thank everyone who has taken the time to contribute. Each PR, bug report, and feature request has made this package better and is truly appreciated.