Twitter Organic dbt Package
This dbt package transforms data from Fivetran's Twitter Organic connector into analytics-ready tables.
Resources
- Number of materialized models¹: 11
- Connector documentation
- dbt package documentation
What does this dbt package do?
This package enables you to transform core social media object tables into analytics-ready models that can be easily unioned with other social media platform packages. It creates enriched models with metrics focused on tweet performance and social media analytics.
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>_twitter_organic
Final output tables
By default, this package materializes the following final tables:
| Table | Description |
|---|---|
| twitter_organic__tweets | Tracks daily performance metrics for your organic tweets to measure engagement, reach, and audience interaction patterns on Twitter/X. Example Analytics Questions:
|
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 Twitter Organic 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.
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.