Social Media Reporting dbt Package

This dbt package transforms data from Fivetran's Social Media Reporting connector into analytics-ready tables.

Resources

• Number of materialized models¹: 6
• Connector documentation
  • Facebook Pages connector documentation
  • Instagram Business connector documentation
  • Twitter Organic connector documentation
  • Linkedin Pages connector documentation
  • Youtube Analytics connector documentation
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog

What does this dbt package do?

This package enables you to aggregate and model data from multiple Fivetran social media connections, standardize schemas from various social media connections, and analyze post performance by clicks, impressions, shares, likes, and comments. It creates enriched models with metrics focused on unified social media reporting across platforms.

Currently, this package supports the following social media connector types:

• Facebook Pages
• Instagram Business
• LinkedIn Company Pages
• Twitter Organic
• Youtube Analytics

NOTE: You do not need to have all of these connector types to use this package, though you should have at least two.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_social_media_reporting

Final output tables

By default, this package materializes the following final tables:

Table

Description

social_media_reporting__rollup_report

Consolidates post performance across multiple social media platforms (Facebook, Instagram, LinkedIn, Twitter, and YouTube Analytics) to compare engagement, reach, and content effectiveness in one unified view. Example Analytics Questions: Which social media platform drives the highest engagement and reach for your content? How does content performance compare across different platforms and account types? What posting strategies work best across your entire social media presence?

Materialized Models

Each Quickstart transformation job run materializes the following model counts for each selected connector. The total model count represents all staging, intermediate, and final models, materialized as view, table, or incremental:

Connector	Model Count
Social Media Reporting	6
Facebook Pages	11
Instagram Business	7
LinkedIn Company Pages	23
Twitter Organic	11
Youtube Analytics	11

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 Social Media Reporting connection syncing data into your destination.
• A BigQuery, Snowflake, Redshift, Postgres, or Databricks destination.

Connector: Have at least one of the below supported Fivetran ad platform connections syncing data into your destination. This package currently supports:

• Facebook Pages
• Instagram Business
• LinkedIn Company Pages
• Twitter Organic
• Youtube Analytics

While you need only one of the above connections to utilize this package, we recommend having at least two to gain the rollup benefit of this package.

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.

Installing the Package

Include the following github package version in your packages.yml

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

packages:
  - package: fivetran/social_media_reporting
    version: [">=1.5.0", "<1.6.0"] # we recommend using ranges to capture non-breaking changes automatically

Do NOT include the upstream social media packages in this file. The transformation package itself has a dependency on it and will install the upstream packages as well.

Do NOT include the individual social media packages in this file. This package has dependencies on the packages and will install them as well.

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']

Configure Database and Schema Variables

By default, this package looks for your social media reporting data in your target database. If this is not where your app platform data is stored, add the relevant <connection>_database variables to your dbt_project.yml file (see below).

vars:
    ##Facebook Pages schema and database variables
    facebook_pages_schema: facebook_pages_schema
    facebook_pages_database: facebook_pages_database

    ##Instagram Business schema and database variables
    instagram_business_schema: instagram_business_schema
    instagram_business_database: instagram_business_database

    ##LinkedIn Pages schema and database variables
    linkedin_pages_schema: linkedin_pages_schema
    linkedin_pages_database: linkedin_pages_database

    ##Twitter Organic schema and database variables
    twitter_organic_schema: twitter_organic_schema
    twitter_organic_database: twitter_organic_database

    ##Youtube Analytics schema and database variables
    youtube_analytics_schema: youtube_analytics_schema
    youtube_analytics_database: youtube_analytics_database

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 the Facebook Pages dbt_project.yml, Instagram Business dbt_project.yml, LinkedIn Company Pages dbt_project.yml, Twitter Organic dbt_project.yml, and Youtube Analytics dbt_project.yml variable declarations to see the expected names.

vars:
    <default_source_table_name>_identifier: your_table_name 

Enabling/Disabling Models

The package assumes that all connector models are enabled, so it will look to pull data from all of the connections listed above. If you don't want to use certain connections, disable those connections' models in this package by setting the relevant variables to false:

vars:
    social_media_rollup__twitter_enabled: False
    social_media_rollup__facebook_enabled: False
    social_media_rollup__linkedin_enabled: False
    social_media_rollup__instagram_enabled: False
    social_media_rollup__youtube_enabled: False

Next, you must disable the models in the unwanted connection's related package, which has its own configuration. Disable the relevant models under the models section of your dbt_project.yml file by setting the enabled value to false.

Only include the models you want to disable. Default values are generally true but that is not always the case.

models:
    # disable instagram business models if not using instagram business
    instagram_business:
        +enabled: false

    # disable linkedin company pages models if not using linkedin company pages
    linkedin_pages:
        +enabled: false

    # disable twitter organic models if not using twitter organic
    twitter_organic:
        +enabled: false

    # disable facebook pages models if not using facebook pages
    facebook_pages:
        +enabled: false

    # disable youtube analytics models if not using youtube analytics
    youtube_analytics:
        +enabled: false

(Optional) Additional configurations

Unioning Multiple Social Media Connections

If you have multiple social media connections in Fivetran, you can use this package on all of them simultaneously. The package will union all of the data together and then pass the unioned table(s) into the reporting model. You will be able to see which source the data came from in the source_relation column of each model. To use this functionality, you will need to set either the union_schemas or union_databases variables:

IMPORTANT: You cannot use both the union_schemas and union_databases variables.

vars:
    ##Schemas variables
    facebook_pages_union_schemas: ['facebook_pages_one','facebook_pages_two']
    linkedin_pages_union_schemas: ['linkedin_company_pages_one', 'linkedin_company_pages_two']
    instagram_business_union_schemas: ['instagram_business_one', 'instagram_business_two', 'instagram_business_three']
    twitter_organic_union_schemas: ['twitter_social_one', 'twitter_social_two', 'twitter_social_three', 'twitter_social_four']
    youtube_analytics_union_schemas: ['youtube_analytics_one','youtube_analytics_two']

    ##Databases variables
    facebook_pages_union_databases: ['facebook_pages_one','facebook_pages_two']
    linkedin_pages_union_databases: ['linkedin_company_pages_one', 'linkedin_company_pages_two']
    instagram_business_union_databases: ['instagram_business_one', 'instagram_business_two', 'instagram_business_three']
    twitter_organic_union_databases: ['twitter_social_one', 'twitter_social_two', 'twitter_social_three', 'twitter_social_four']
    youtube_analytics_union_databases: ['youtube_analytics_one','youtube_analytics_two']

For more configuration information, see the individual connector dbt packages (listed above).

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/facebook_pages
      version: [">=1.2.0", "<1.3.0"]

    - package: fivetran/instagram_business
      version: [">=1.1.0", "<1.2.0"]

    - package: fivetran/twitter_organic
      version: [">=1.1.0", "<1.2.0"]

    - package: fivetran/linkedin_pages
      version: [">=1.3.0", "<1.4.0"]

    - package: fivetran/youtube_analytics
      version: [">=1.1.0", "<1.2.0"]

    - 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 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 encounter any 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 future dbt package to be developed, then feel free to fill out our Feedback Form.