Youtube Analytics Transformation dbt Package (Docs)

What does this dbt package do?

• Produces modeled tables that leverage data in the format described by the YouTube Channel Report schemas and builds off the output of our Youtube Analytics source package.
• Transform the core object tables into analytics-ready models.
• Includes options to explore video demographics and a comprehensive overview of video performance that you could combine with other organic ad platform reports.
• Generates a comprehensive data dictionary of your source and modeled Youtube Analytics data through the dbt docs site.

The following table provides a detailed list of all tables materialized within this package by default.

TIP: See more details about these tables in the package's dbt docs site.

Table	Description
youtube__video_report	Each record represents the daily aggregation of your YouTube video performance.
youtube__demographics_report	Each record represents a daily video view percentage by gender, age, and country.
youtube__age_demographics_pivot	Each record represents a daily video view percentage with the age ranges pivoted out for quicker analysis.
youtube__gender_demographics_pivot	Each record represents a daily video view percentage with the gender options pivoted out for quicker analysis.
youtube__video_metadata	Each record represents an individual video enriched with metadata.

Materialized Models

Each Quickstart transformation job run materializes 11 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.

How do I use the dbt package?

Step 1: 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.

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']

Step 2: 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: [">=0.4.0", "<0.5.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the youtube_analytics_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.

Step 3: Define database and schema variables

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 schema is named youtube_analytics_fivetran), add the following configuration to your root dbt_project.yml file:

# dbt_project.yml
vars:
    youtube_analytics_schema: your_schema_name
    youtube_analytics_database: your_database_name 

Step 4: 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) Step 5: 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 blank for just the target_schema
  youtube_analytics_source:
    +schema: my_new_schema_name # leave blank for just the 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 

(Optional) Step 6: 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/youtube_analytics_source
      version: [">=0.4.0", "<0.5.0"]
    - 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 that 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.

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.