Fivetran Platform dbt Package

This dbt package transforms data from the Fivetran Platform connector into analytics-ready tables.

Resources

• Number of materialized models¹: 19
• Connector documentation
  • Fivetran Platform connector documentation
  • Fivetran Platform ERD
• dbt package documentation
  • GitHub repository
  • dbt Docs
  • DAG
  • Changelog

What does this dbt package do?

This package enables you to better understand how you are spending money in Fivetran according to our consumption-based pricing model and provides details about the performance and status of your Fivetran connections. It creates enriched models with metrics focused on consumption data, monthly active rows (MAR), credit consumption, connection events, schema changes, and audit logs.

Output schema

Final output tables are generated in the following target schema:

<your_database>.<connector/schema_name>_fivetran_platform

Final output tables

By default, this package materializes the following final tables:

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:

• A Fivetran Platform connection syncing data into your destination.
• A BigQuery, Snowflake, Redshift, Postgres, Databricks, or SQL Server 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 Fivetran Platform package version range in your packages.yml

Check dbt Hub for the latest installation instructions, or read the dbt docs for more information on installing packages.

dbt Core >= 1.9.6 is required to run freshness tests out of the box. See other options here.

packages:
  - package: fivetran/fivetran_log
    version: [">=2.5.0", "<2.6.0"]

Note that although the source connector is now "Fivetran Platform", the package retains the old name of "fivetran_log".

Databricks Dispatch 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']

Database Incremental Strategies

For models in this package that are materialized incrementally, they are configured to work with the different strategies available to each supported warehouse.

For BigQuery and Databricks All Purpose Cluster runtime destinations, we have chosen insert_overwrite as the default strategy, which benefits from the partitioning capability.

For Databricks SQL Warehouse destinations, we have chosen merge as the default strategy.

For Snowflake, Redshift, and Postgres destinations, we have chosen delete+insert as the default strategy.

Regardless of strategy, we recommend that users periodically run a --full-refresh to ensure a high level of data quality.

Define Database and Schema Variables

By default, this package will run using your target database and the fivetran_log schema. If this is not where your Fivetran Platform data is (perhaps your fivetran platform schema is fivetran_platform), add the following configuration to your root dbt_project.yml file:

vars:
    fivetran_platform_database: your_database_name # default is your target.database
    fivetran_platform_schema: your_schema_name # default is fivetran_log

Disable Models for Non Existent Sources

If you do not leverage Fivetran RBAC, then you will not have the user or destination_membership source tables. The user and destination_membership are enabled by default. Therefore in order to switch the default configurations, you must add the following variable(s) to your root dbt_project.yml file for the respective source tables you wish to disable:

vars:
    fivetran_platform_using_destination_membership: false # Default is true. This will disable only the destination membership logic
    fivetran_platform_using_user: false # Default is true. This will disable only the user logic

Leveraging CONNECTION vs CONNECTOR

In Q1 2025, the CONNECTOR source table was deprecated and replaced by CONNECTION, and CONNECTION is now the default source.

• For Quickstart users, CONNECTOR will automatically be used if CONNECTION is not yet available.
• For dbt Core users, if CONNECTION is not yet available in your connection, you can continue using CONNECTOR by adding the following variable to your root dbt_project.yml file:

vars:
    fivetran_platform_using_connection: false # default: true

(Optional) Additional Configurations

Change the Build Schema

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

models:
  fivetran_log:
    +schema: my_new_final_models_schema # leave blank for just the target_schema
    staging:
      +schema: my_new_staging_models_schema # leave blank for just the target_schema

Change the Source Table References

If an individual source table has a different name than expected (see this projects dbt_project.yml variable declarations for expected names), provide the name of the table as it appears in your warehouse to the respective variable as identified below:

vars:
    fivetran_platform_<default_table_name>_identifier: your_table_name 

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

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 below packages, refer to the dbt hub site.

If you have any of these dependent packages in your own packages.yml I highly recommend you remove them to ensure there are no 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.