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

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:

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: [">=1.1.0", "<1.2.0"] # 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

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 

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

Unioning Multiple Youtube Analytics Connections

If you have multiple Youtube Analytics connections in Fivetran and want to use this package on all of them simultaneously, we have provided functionality to do so. The package will union all of the data together and pass the unioned table(s) into the final models. You will be able to see which source it came from in the source_relation column(s) of each model. To use this functionality, you will need to set either (note that you cannot use both) the union_schemas or union_databases variables:

# dbt_project.yml
vars:
    ##You may set EITHER the schemas variables below
    youtube_analytics_union_schemas: ['youtube_analytics_one','youtube_analytics_two']

    ##Or may set EITHER the databases variables below
    youtube_analytics_union_databases: ['youtube_analytics_one','youtube_analytics_two']

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 

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

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.