YouTube Analytics Source dbt (Docs)

What does this dbt package do?

• Materializes Youtube Analytics staging tables which leverage data in the format described by the YouTube Channel Report schemas. These staging tables clean, test, and prepare your Youtube Analytics data from Fivetran's connector for analysis by doing the following:
  • Name columns for consistency across all packages and for easier analysis
  • Adds freshness tests to source data
  • Adds column-level testing where applicable. For example, all primary keys are tested for uniqueness and non-null values.
• Generates a comprehensive data dictionary of your Youtube Analytics data through the dbt docs site.
• These tables are designed to work simultaneously with our Youtube Analytics transformation package.

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 connector 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 (skip if also using the youtube_analytics transformation package)

Include the following youtube_analytics_source package version in your packages.yml file.

Do NOT add this if you have added the Youtube Analytics Transformation package to your packages.yml file. 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_source
    version: [">=0.4.0", "<0.5.0"] # we recommend using ranges to capture non-breaking changes automatically

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: Enable/Disable 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 builds the Youtube Analytics staging models within a schema titled (<target_schema> + _youtube_source) in your destination. If this is not where you would like your Youtube staging data to be written to, add the following configuration to your root dbt_project.yml file:

# dbt_project.yml
models:
    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™

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 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.