Linkedin Pages Source dbt Package (Docs)
What does this dbt package do?
- Materializes LinkedIn Pages staging tables which leverage data in the format described by this ERD. These staging tables clean, test, and prepare your LinkedIn Pages 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 LinkedIn Pages data through the dbt docs site.
- These tables are designed to work simultaneously with our LinkedIn Pages transformation package and our Social Media Reporting package.
How do I use the dbt package?
Step 1: Prerequisites
To use this dbt package, you must have the following:
- A Fivetran LinkedIn Pages connector syncing data into your destination.
- A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.
Databricks Additional 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 Linkedin Pages transformation package)
If you are not using the Linkedin Pages transformation package, include the following package version in your packages.yml file. If you are installing the transform package, the source package is automatically installed as a dependency.
TIP: Check dbt Hub for the latest installation instructions or read the dbt docs for more information on installing packages.
packages:
- package: fivetran/linkedin_pages_source
version: [">=0.3.0", "<0.4.0"]
Step 3: Define database and schema variables
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:
vars:
linkedin_pages_schema: your_schema_name
linkedin_pages_database: your_database_name
(Optional) Step 4: Additional Configurations
Expand for configurations
Changing the Build Schema
By default, this package will build the LinkedIn Pages staging models within a schema titled (<target_schema> + _stg_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:
models:
linkedin_pages_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.ymlvariable declarations to see the expected names.
vars:
linkedin_pages_<default_source_table_name>_identifier: your_table_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']
(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™
Expand for details
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.
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.ymlfile, we highly recommend that you remove them from your rootpackages.ymlto 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 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. Check out this dbt Discourse article on the best workflow for contributing to a package.
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.