Youtube Analytics dbt Package
This dbt package transforms data from Fivetran's Youtube Analytics connector into analytics-ready tables.
Resources
- Number of materialized models¹: 11
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to transform core object tables into analytics-ready models and explore video demographics. It creates enriched models with metrics focused on video performance and demographic insights.
Output schema
Final output tables are generated in the following target schema:
<your_database>.<connector/schema_name>_youtube_analytics
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| youtube__video_report | Tracks daily video performance metrics including views, watch time, engagement, and revenue to analyze content performance and audience behavior. Example Analytics Questions:
|
| youtube__demographics_report | Breaks down daily video views by audience demographics including gender, age group, and country to understand who is watching your content. Example Analytics Questions:
|
| youtube__age_demographics_pivot | Provides daily video view percentages with age ranges pivoted into separate columns for streamlined demographic analysis and reporting. Example Analytics Questions:
|
| youtube__gender_demographics_pivot | Shows daily video view percentages with gender segments pivoted into separate columns for quick gender-based audience analysis. Example Analytics Questions:
|
| youtube__video_metadata | Provides comprehensive video metadata including titles, descriptions, tags, publication dates, and channel information to enrich video performance analysis. Example Analytics Questions:
|
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 Youtube Analytics 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.