Facebook Pages dbt Package

This dbt package transforms data from Fivetran's Facebook Pages connector into analytics-ready tables.

Resources

Number of materialized models¹: 11
Connector documentation
- Facebook Pages connector documentation
- Facebook Pages ERD
dbt package documentation

What does this dbt package do?

This package enables you to transform core social media object tables into analytics-ready models and generate comprehensive data dictionaries. It creates enriched models with metrics focused on daily page performance and post performance.

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 aided by our Social Media Reporting package.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_facebook_pages

Final output tables

By default, this package materializes the following final tables:

Table	Description
facebook_pages__pages_report	Tracks daily performance metrics for your Facebook Pages to measure audience growth, engagement, and reach across your brand presence. Example Analytics Questions: How are follower additions and removals trending over time for each page? Which pages generate the highest post engagement and views? What is the video view completion rate (30-second views vs total views) by page?
facebook_pages__posts_report	Analyzes daily performance for individual Facebook posts to understand which content resonates most with your audience through likes, comments, shares, and reach metrics. Example Analytics Questions: Which posts generate the highest click-through rates and views? How do video posts perform in terms of average watch time and 10-second view rates? What types of post messages or content drive the most likes and engagement?

Table

Description

facebook_pages__pages_report

Tracks daily performance metrics for your Facebook Pages to measure audience growth, engagement, and reach across your brand presence.

Example Analytics Questions:

How are follower additions and removals trending over time for each page?
Which pages generate the highest post engagement and views?
What is the video view completion rate (30-second views vs total views) by page?

facebook_pages__posts_report

Analyzes daily performance for individual Facebook posts to understand which content resonates most with your audience through likes, comments, shares, and reach metrics.

Example Analytics Questions:

Which posts generate the highest click-through rates and views?
How do video posts perform in terms of average watch time and 10-second view rates?
What types of post messages or content drive the most likes and engagement?

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 Facebook Pages connection syncing data into your destination.
A BigQuery, Snowflake, Redshift, PostgreSQL, or Databricks destination.

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 Facebook 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/facebook_pages
    version: [">=1.2.0", "<1.3.0"] # we recommend using ranges to capture non-breaking changes automatically

All required sources and staging models are now bundled into this transformation package. Do not include fivetran/facebook_pages_source in your packages.yml since this package has been deprecated.

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 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']

Configure Your Variables

Database and Schema Variables

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

vars:
    facebook_pages_schema: your_schema_name
    facebook_pages_database: your_database_name

(Optional) Additional Configurations

Expand for configurations

Changing the Build Schema

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

models:
    facebook_pages:
      +schema: my_new_schema_name # Leave +schema: blank to use the default target_schema.
      staging:
        +schema: my_new_schema_name # Leave +schema: blank to use the default 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:
    facebook_pages_<default_source_table_name>_identifier: your_table_name

Unioning Multiple Facebook Pages Connections

If you have multiple Facebook Pages connections 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
    facebook_pages_union_schemas: ['facebook_pages_one','facebook_pages_two']

    ##Or may set EITHER the databases variables below
    facebook_pages_union_databases: ['facebook_pages_one','facebook_pages_two']

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

Expand for configurations

Fivetran offers the ability for you to orchestrate your dbt project through the Fivetran Transformations for dbt Core™ 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.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

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.