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
- dbt package documentation
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:
| Table | Description |
|---|---|
| fivetran_platform__connection_status | Provides a comprehensive view of each connection loading data into your destinations, enriched with detailed information about sync status, sync frequency, setup status, and connection health to monitor and troubleshoot your data pipeline performance. |
| fivetran_platform__mar_table_history | Tracks a table's monthly free, paid, and total volume breakdowns, with connection and destination details to analyze your data consumption patterns and costs at the table level over time. |
| fivetran_platform__usage_history | Summarizes each destination's monthly usage and active volume with calculated metrics for usage per million MAR and MAR per usage unit to track your Fivetran consumption costs and efficiency. Usage represents either dollar or credit amounts depending on your pricing model. Read more about the relationship between usage and MAR here. |
| fivetran_platform__connection_daily_events | Captures daily operational metrics for each connection including API calls made, schema changes implemented, and record modifications processed, starting from the connection setup date to provide insights into connection activity patterns and data processing volumes. |
| fivetran_platform__schema_changelog | Documents all schema changes made to your connections including table alterations, table creations, schema creations, and configuration changes with detailed metadata about each event to track data structure evolution and troubleshoot schema-related issues. |
| fivetran_platform__audit_table | Replaces the deprecated _fivetran_audit table and tracks each table receiving data during connection syncs with comprehensive timestamps for connection and table-level sync progress plus detailed counts of records inserted, replaced, updated, and deleted to monitor data processing and sync performance. |
| fivetran_platform__audit_user_activity | Records all user-triggered actions within your Fivetran account to provide a comprehensive audit trail that helps you trace user activities to specific log events such as schema changes, sync frequency updates, manual syncs, connection failures, and other operational events for compliance and troubleshooting purposes. |
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.
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.