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
  • Youtube Analytics connector documentation
  • Youtube Analytics ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog
• dbt Core™ supported versions
  • >=1.3.0, <3.0.0

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: Which videos generate the most views, watch time, and subscriber growth? How do engagement rates (likes, comments, shares) vary across different videos? What is the average view duration and audience retention by video?
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: What percentage of views come from different age groups or gender segments? Which countries drive the most video views and watch time? How do demographic distributions vary across different videos or content types?
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: Which age groups have the highest view percentages for each video? How do age demographics vary between different content categories? What is the age distribution trend over time for popular videos?
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: What is the gender split of viewers for each video? How do gender demographics differ across content types or topics? Are there gender preference patterns for specific video categories?
youtube__video_metadata	Provides comprehensive video metadata including titles, descriptions, tags, publication dates, and channel information to enrich video performance analysis. Example Analytics Questions: Which video tags or categories are associated with the highest-performing content? How do video titles or descriptions correlate with view counts and engagement? What is the publication date distribution and video count by channel?

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.

Install the package

Include the following Youtube Analytics package version in your packages.yml file:

TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages

# packages.yml
packages:
  - package: fivetran/youtube_analytics
    version: [">=1.2.0", "<1.3.0"] # we recommend using ranges to capture non-breaking changes automatically

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/youtube_analytics_source in your packages.yml since this package has been deprecated.

Databricks Dispatch Configuration

If you are using a Databricks destination with this package you will need to add the below (or a variation of the below) dispatch configuration within your dbt_project.yml. This is required in order for the package to accurately search for macros within the dbt-labs/spark_utils then the dbt-labs/dbt_utils packages respectively.

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Define database and schema variables

Option A: Single connection

By default, this package runs using your destination and the youtube_analytics schema. If this is not where your Youtube Analytics data is (for example, if your Youtube Analytics schema is named youtube_analytics_fivetran), add the following configuration to your root dbt_project.yml file:

vars:
    youtube_analytics_database: your_destination_name
    youtube_analytics_schema: your_schema_name

Option B: Union multiple connections

If you have multiple Youtube Analytics connections in Fivetran and would like to use this package on all of them simultaneously, we have provided functionality to do so. For each source table, the package will union all of the data together and pass the unioned table into the transformations. The source_relation column in each model indicates the origin of each record.

To use this functionality, you will need to set the youtube_analytics_sources variable in your root dbt_project.yml file:

# dbt_project.yml

vars:
  youtube_analytics:
    youtube_analytics_sources:
      - database: connection_1_destination_name # Required
        schema: connection_1_schema_name # Required
        name: connection_1_source_name # Required only if following the step in the following subsection

      - database: connection_2_destination_name
        schema: connection_2_schema_name
        name: connection_2_source_name

Previous versions of this package employed two separate, mutually exclusive variables for unioning: youtube_analytics_union_schemas and youtube_analytics_union_databases. While these variables are still supported, youtube_analytics_sources is the recommended variable to configure.

Optional: Incorporate unioned sources into DAG

If you use Fivetran Transformations for dbt Core™ and are unioning multiple Youtube Analytics connections, you can define your sources in a property .yml file, using this as a template. Set the variable has_defined_sources: true under the Youtube Analytics namespace in your dbt_project.yml. Otherwise, your Youtube Analytics connections won't appear in your DAG. See the union_connections macro documentation for full configuration details.

Disabling Demographics Report

This packages assumes you are syncing the YouTube channel_demographics_a1 report. If you are not syncing this report, you may add the below configuration to your dbt_project.yml to disable the stg_youtube__demographics model and all downstream references.

# dbt_project.yml

vars:
  youtube__using_channel_demographics: false # true by default

(Optional) Additional configurations

Change the build schema

By default, this package will build the YouTube Analytics staging models within a schema titled (<target_schema> + _youtube_source) and the YouTube Analytics final models within a schema titled (<target_schema> + _youtube) in your target database. If this is not where you would like your modeled YouTube Analytics data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml
models:
    youtube_analytics:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.

Change the source table references

If an individual source table has a different name than the package expects, add the table name as it appears in your destination to the respective variable:

IMPORTANT: See this project's dbt_project.yml variable declarations to see the expected names.

# dbt_project.yml
vars:
    youtube_analytics_<default_source_table_name>_identifier: your_table_name 

Source casing for case-sensitive destinations

By default, the package applies case-insensitive comparisons when resolving source_relation values. If your destination is case-sensitive and you want downstream transformations to respect the exact casing of your source database and schema names, set the following variable:

vars:
    fivetran_using_source_casing: true

(Optional) Orchestrate your models with Fivetran Transformations for dbt Core™

Expand for details

Fivetran offers the ability for you to orchestrate your dbt project through Fivetran Transformations for dbt Core™. Learn how to set up your project for orchestration through Fivetran in our Transformations for dbt Core™ setup guides.

Does this package have dependencies?

This dbt package is dependent on the following dbt packages. These dependencies are installed by default within this package. For more information on the following packages, refer to the dbt hub site.

IMPORTANT: If you have any of these dependent packages in your own packages.yml file, we highly recommend that you remove them from your root packages.yml to avoid package version conflicts.

packages:
    - package: fivetran/fivetran_utils
      version: [">=0.4.0", "<0.5.0"]
    - package: dbt-labs/dbt_utils
      version: [">=1.0.0", "<2.0.0"]
    - package: dbt-labs/spark_utils
      version: [">=0.3.0", "<0.4.0"]

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.

Are there any resources available?

• If you have questions or want to reach out for help, see the GitHub Issue section to find the right avenue of support for you.
• If you would like to provide feedback to the dbt package team at Fivetran or would like to request a new dbt package, fill out our Feedback Form.