YouTube Analytics

This package models YouTube Analytics data from Fivetran’s connector. It uses data in the format described by the YouTube Channel Report schemas.

The main focus of the package is to transform the core object tables into analytics-ready models. It includes options to explore video demographics and a comprehensive overview of video performance that you could combine with other organic ad platform reports.

Modelslink

This package contains transformation models, designed to work simultaneously with our YouTube Analytics source package. A dependency on the source package is declared in this package’s packages.yml file, so it will automatically download when you run dbt deps. The primary outputs of this package are described below.

Installation Instructionslink

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

Include in your packages.yml

packages:
  - package: fivetran/youtube_analytics
    version: [">=0.2.0", "<0.3.0"]

Configurationlink

Required YouTube Channel Reportslink

To use this package, you need to pull the following YouTube Analytics reports through Fivetran:

• channel_basic_a2 (required)
• channel_demographics_a1 (optional)
• videos metadata table (optional)

Schema Configurationlink

By default, this package will look for your YouTube Analytics data in the youtube_analytics schema of your target database. If this is not where your YouTube Analytics data is, please add the following configuration to your dbt_project.yml file:

...
config-version: 2

vars:
    youtube_analytics_database: your_database_name
    youtube_analytics_schema: your_schema_name

YouTube Video Metadatalink

The Fivetran YouTube Analytics connector currently does not support video metadata. Consequently, it may be difficult to analyze individual video data without knowing which video belongs to which record.

As a workaround, you can create a Functions connector that syncs your YouTube video metadata into a table in your destination. This dbt package can then use the VIDEOS metadata table to enrich your YouTube Analytics reporting data. To learn more about creating a Functions connector, read our YouTube Analytics Video Metadata Cloud Function article. It provides code and detailed steps on how to configure the function.

Enable Video Metadatalink

By default, the video metadata functionality within this package is disabled. If you have configured a cloud function to sync your video metadata into a VIDEOS table, you must enable the package to incorporate the metadata into your package. You may use the variable configuration below in your dbt_project.yml to enable this functionality:

vars:
  youtube__using_video_metadata: true # false by default

Video Metadata Schema Configurationlink

By default, this package will look for your VIDEOS YouTube Analytics metadata table in the youtube_analytics_metadata schema of your target database. If this is not where your YouTube Analytics metadata table is, please add the following configuration to your dbt_project.yml file:

...
config-version: 2

vars:
    youtube_metadata_schema: your_schema_name
    youtube_analytics_database: your_database_name 

Disable Demographics Reportlink

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.

...
vars:
  youtube__using_channel_demographics: false # true by default

Specifying Source Table Nameslink

This package assumes that the channel_basic_a_2 and channel_demographics_a_1 reports are named accordingly. If these reports have different names in your destination, enter the correct names in the channel_basic_table_name and/or channel_demographics_table_name variables in your dbt_project.yml so that the package can find them:

...
vars:
  youtube__channel_basic_table:         "my_channel_basic_table_name"
  youtube__channel_demographics_table:  "demographics_youtube_report"

Changing the Build Schemalink

By default, this package will build the YouTube Analytics staging models within a schema titled (<target_schema> + _stg_youtube) 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:

...
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

For additional configurations for the source models, visit the YouTube Analytics source package.

Contributionslink

Additional contributions to this package are very welcome! Please create issues or open PRs against main. Check out this post on the best workflow for contributing to a package.

Database supportlink

This package has been tested on BigQuery, Snowflake, Redshift, Postgres, and Databricks.

Databricks Dispatch Configurationlink

dbt v0.20.0 introduced a new project-level dispatch configuration that enables an “override” setting for all dispatched macros. 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']

Resources:link

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