LinkedIn Pages Modeling dbt Package (Docs)
- Produces modeled tables that leverage LinkedIn Pages data from Fivetran's connector in the format described by this ERD and builds off the output of our LinkedIn Pages source package.
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.
This package also generates a comprehensive data dictionary of your source and modeled Salesforce data via the dbt docs site.
You can also refer to the table below for a detailed view of all tables materialized by default within this package.
| Table | Description |
|---|---|
| linkedin_pages__posts | Each record represents the performance of a LinkedIn post |
You will need to ensure you have the following before leveraging the dbt package.
- Connector: Have the Fivetran LinkedIn Pages connector syncing data into your warehouse.
- Database support: This package has been tested on BigQuery, Snowflake, Redshift, Databricks, and Postgres. Ensure you are using one of these supported databases.
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 root 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']Include the following LinkedIn Pages 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/linkedin_pages
version: [">=0.3.0", "<0.4.0"]Do NOT include the linked_pages_source package in this file. The transformation package itself has a dependency on it and will install the source package as well.
By default, this package will run using your target database and the linkedin_pages schema. If this is not where your LinkedIn Pages data is, please add the following configuration to your dbt_project.yml file:
vars:
linkedin_pages_schema: your_schema_name
linkedin_pages_database: your_database_name Expand for configurations
By default, this package builds the GitHub staging models within a schema titled (<target_schema> + _stg_linkedin_pages) in your target database. If this is not where you would like your GitHub staging data to be written to, add the following configuration to your root dbt_project.yml file:
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_schemaSource tables are referenced using default names. If an individual source table has a different name than expected, provide the name of the table as it appears in your warehouse to the respective variable:
IMPORTANT: See the package's source
dbt_project.ymlvariable declarations to see the expected names.
vars:
<package_name>__<default_source_table_name>_identifier: your_table_nameIf 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']Expand for configurations
Fivetran offers the ability for you to orchestrate your dbt project through the [Fivetran Transformations for dbt Core™](https://fivetran.com/docs/transformations/dbt) product. Refer to the linked docs for more information on how to setup your project for orchestration through Fivetran.
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.ymlfile, we highly recommend that you remove them from your rootpackages.ymlto avoid package version conflicts.
packages:
- package: fivetran/linkedin_pages_source
version: [">=0.3.0", "<0.4.0"]
- 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"]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.
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. Check out this dbt Discourse article on the best workflow for contributing to a package.
- 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.