LinkedIn Pages

This package models LinkedIn Pages data from Fivetran's connector. It uses data in the format described by this ERD.

The main focus of the package is to transform the core social media object tables into analytics-ready models that can be easily unioned in to other social media platform packages to get a single view. This is especially easy using our Social Media Reporting package.

Models

This package contains transformation models, designed to work simultaneously with our LinkedIn Pages source package and our multi-platform Social Media Reporting 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.

model	description
linkedin_pages__posts	Each record represents the performance of a LinkedIn post

Installation Instructions

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/linkedin_pages
    version: [">=0.1.0", "<0.2.0"]

Package Maintenance

The Fivetran team maintaining this package only maintains the latest version. We highly recommend you keep your packages.yml updated with the dbt hub latest version. You may refer to the CHANGELOG and release notes for more information on changes across versions.

Configuration

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

# dbt_project.yml

...
config-version: 2

vars:
    linkedin_pages_schema: your_schema_name
    linkedin_pages_database: your_database_name 

Unioning Multiple LinkedIn Pages Connectors

If you have multiple LinkedIn Pages connectors in Fivetran and would like 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
...
config-version: 2
vars:
    ##You may set EITHER the schemas variables below
    linkedin_pages_union_schemas: ['linkedin_pages_one','linkedin_pages_two']

    ##OR you may set EITHER the databases variables below
    linkedin_pages_union_databases: ['linkedin_pages_one','linkedin_pages_two']

Changing the Build Schema

By default, this package will build the LinkedIn Pages staging models within a schema titled (<target_schema> + _stg_linkedin_pages) and the final LinkedIn Pages models within a schema titled (<target_schema> + _linkedin_pages) in your target database. If this is not where you would like your LinkedIn Pages staging data to be written to, add the following configuration to your dbt_project.yml file:

# dbt_project.yml

...
models:
    linkedin_pages:
      +schema: my_new_schema_name # leave blank for just the target_schema
    linkedin_pages_source:
      +schema: my_new_schema_name # leave blank for just the target_schema

Contributions

Don't see a model or specific metric you would like to be included? Notice any bugs when installing and running the package? If so, we highly encourage and welcome contributions to this package! Please create issues or open PRs against main. See the Discourse post for information on how to contribute to a package.

Database Support

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

Databricks Dispatch Configuration

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.

# dbt_project.yml

dispatch:
  - macro_namespace: dbt_utils
    search_order: ['spark_utils', 'dbt_utils']

Resources:

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• Check out Fivetran's blog
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• Check out Discourse for commonly asked questions and answers
• Join the chat on Slack for live discussions and support
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