Release Notes

August 2026

v2.5.3

Feature Updates

• Adds DuckDB as a supported destination.

April 2026

v2.5.2

Under the Hood

• Adds get_*_columns() macros for all staging source tables and updates the corresponding staging models to use fivetran_utils.fill_staging_columns(). Staging models now handle missing source columns gracefully by filling them with null rather than raising an error.
• Adjusts row_count__connection_daily_events test to dynamically reference connection or connector source tables

February 2026

v2.5.1

Feature Update

• Converts the message_data field in the stg_fivetran_platform__log model to a string for the following datatypes:
  • Snowflake: VARIANT
  • Redshift: SUPER
  • Postgres: JSON and JSONB
  • BigQuery: JSON (already supported)

Under the Hood

• Expands testing of JSON datatypes in the stg_fivetran_platform__log model.

January 2026

v2.5.0

Features

• Increases the required dbt version upper limit to v3.0.0

November 2025

v2.4.0

This release includes updates from pre-release versions 2.3.2-a1 and 2.4.0-a1.

Feature Update

• Optimizes the fivetran_platform__audit_table model logic to run in a timelier manner on full refreshes. (PR #174)

Bug Fix

• Resolves duplicate records in fivetran_platform__audit_table arising out of multiple records_modified events for a single table during a sync. (PR #174)

Under the Hood

• Rolls back additions to the quickstart.yml made in v2.2.1. We now rely on the does_table_exist() macro to handle missing credits_used or transformation_runs tables. (PR #172)

September 2025

v2.3.1

Bug Fix

• Ensures that return() is only called once in the is_databricks_all_purpose_cluster() macro. This resolves the following error that dbt Fusion users may have received:

Failed to add template invalid operation: return() is called in a non-block context
(in fivetran_log.default__is_databricks_all_purpose_cluster:7:12)
  --> fivetran_log.default__is_databricks_all_purpose_cluster:7:12