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.yml variable 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. Please be aware that 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/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

These dbt packages are developed by a small team of analytics engineers at Fivetran. However, the packages are made better by community contributions!

We highly encourage and welcome contributions to this package. Check out this post on the best workflow for contributing to a package!

🏪 Are there any resources available?

If you encounter any questions or want to reach out for help, please refer to 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.
Have questions or want to just say hi? Book a time during our office hours here or send us an email at [email protected].

[Bug] Organization name not passing through for ugc posts

Is there an existing issue for this?

I have searched the existing issues

Describe the issue

The existing logic in the stg_linkedin_pages__organization_ugc_post model is only effective in parsing the ugc_post_id from share type posts. As a result, downstream models for ugcpost types having breaking joins. I noticed this when the organization_name came up as null for these types of posts.

The existing logic looks like:
replace(ugc_post_id, 'urn:li:share:', '') as ugc_post_id

Example output in stg_linkedin_pages__organization_ugc_post

Example output in linkedin_pages__post:

The logic is correct in stg_linkedin_pages__ugc_post_history since it considers both post types. As a result, the ugc_post_id looks right in linkedin_pages__posts, but the join with organization_ugc_post breaks due to mismatching ugc_post_id.

Relevant error log or model output

No response

Expected behavior

Proposed logic would look like:
split_part(ugc_post_id, ':', -1) as ugc_post_id

Example output of stg_linkedin_pages__organization_ugc_post with proposed fix:

Example output of linkedin_pages__posts with proposed fix:

This will make the logic more dynamic, and effectively parse the ugc_post_id from both share and ugcpost types.

dbt Project configurations

vars:
 
# Social Media Reporting
  social_media_rollup__linkedin_enabled: True

# LinkedIn Organic
  linkedin_pages_database: raw
  linkedin_pages_schema: fivetran_linkedin_organic

  
models:
  # disable both linkedin company pages models if not using linkedin company pages
  linkedin_pages:
      enabled: true
  linkedin_pages_source:
      enabled: true

Package versions

  - package: fivetran/social_media_reporting
    version: [">=0.1.0", "<0.2.0"]

What database are you using dbt with?

snowflake

dbt Version

installed version: 1.0.3
   latest version: 1.0.3

Up to date!

Plugins:
  - snowflake: 1.0.0 - Up to date!

Additional Context

No response

Are you willing to open a PR to help address this issue?

Yes.
Yes, but I will need assistance and will schedule time during our office hours for guidance
No.

fivetran / dbt_linkedin_pages_source Goto Github PK

dbt_linkedin_pages_source's Introduction

Linkedin Pages Source dbt Package (Docs)

📣 What does this dbt package do?

🎯 How do I use the dbt package?

Step 1: Prerequisites

Databricks Additional Configuration

Step 2: Install the package (skip if also using the Linkedin Pages transformation package)

Step 3: Define database and schema variables

(Optional) Step 4: Additional Configurations

Changing the Build Schema

Change the source table references

Unioning Multiple LinkedIn Pages Connectors

(Optional) Step 5: Orchestrate your models with Fivetran Transformations for dbt Core™

🔍 Does this package have dependencies?

🙌 How is this package maintained and can I contribute?

Package Maintenance

Contributions

🏪 Are there any resources available?

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dbt_linkedin_pages_source's Issues

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Describe the issue

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Is there an existing issue for this?

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dbt Project configurations

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dbt Version

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Is there an existing issue for this?

Describe the issue

Relevant error log or model output

Expected behavior

dbt Project configurations

Package versions

What database are you using dbt with?

dbt Version

Additional Context

Are you willing to open a PR to help address this issue?

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Step 2: Install the package (skip if also using the `Linkedin Pages` transformation package)