Instagram Business dbt Package

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

Resources

Number of materialized models¹: 7
Connector documentation
- Instagram Business connector documentation
- Instagram Business 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 post and story performance that can be easily unioned with other social media platform packages.

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>_instagram_business

Final output tables

By default, this package materializes the following final tables:

Table	Description
instagram_business__posts	Tracks daily performance metrics for your Instagram posts and stories to measure engagement, reach, and content effectiveness across your feed. Example Analytics Questions: Which posts or stories generate the highest engagement rates by content type or time of day? How does reach and impression growth compare across different posting strategies? What types of content (posts vs stories) drive the most follower interaction?

Table

Description

instagram_business__posts

Tracks daily performance metrics for your Instagram posts and stories to measure engagement, reach, and content effectiveness across your feed.

Example Analytics Questions:

Which posts or stories generate the highest engagement rates by content type or time of day?
How does reach and impression growth compare across different posting strategies?
What types of content (posts vs stories) drive the most follower interaction?

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 Instagram Business 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.

Install the Package

Include the following Instagram Business 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/instagram_business
    version: [">=1.1.0", "<1.2.0"]

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 Instagram Business data in the instagram_business schema of your target database. If this is not where your Instagram Business data is, add the following configuration to your dbt_project.yml file:

vars:
    instagram_business_schema: your_schema_name
    instagram_business_database: your_database_name

(Optional) Additional Configurations

Expand for configurations

Change the Build Schema

By default, this package builds the Instagram Business staging models within a schema titled (<target_schema> + _stg_instagram_business) in your target database. If this is not where you would like your Instagram Business staging data to be written to, add the following configuration to your root dbt_project.yml file:

models:
    instagram_business:
      +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:
    instagram_business_<default_source_table_name>_identifier: your_table_name

Unioning Multiple Instagram Business Connections

If you have multiple Instagram Business connections in Fivetran and want 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
    instagram_business_union_schemas: ['instagram_business_one','instagram_business_two']

    ##Or may set EITHER the databases variables below
    instagram_business_union_databases: ['instagram_business_one','instagram_business_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™](https://fivetran.com/docs/transformations/dbt#transformationsfordbtcore) 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 have questions or want to reach out for help, 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 new dbt package, fill out our Feedback Form.