fivetran/dbt_fivetran_log v2.3.0 on GitHub

PR #164 includes the following updates:

Schema Data Changes

3 total changes • 3 possible breaking changes

To prevent potential errors from naming and materialization updates, a --full-refresh is required after upgrading.

Data Models	Change Type	Old	New	Notes
`fivetran_platform__audit_table`	Model materialization (only for Databricks SQL Warehouse runtimes)	table	incremental	Added incremental model support for Databricks SQL Warehouses using the `merge` strategy. Previously, incremental support was limited to Databricks All Purpose Clusters via the `insert_overwrite` strategy. This update extends incremental functionality to SQL Warehouses, enabling more efficient model builds.
`fivetran_platform__audit_table`	New column		`write_to_table_start_day`	Changed the column partitioned on from `sync_start_day` to the new `write_to_table_start_day`. The previous column could contain null values, which are not ideal for partitioning and may lead to unexpected behavior in incremental models.
`fivetran_platform__audit_table`	Deleted column	`sync_start_day`		No longer in use given the above.

Updated fivetran_platform__mar_table_history to include consumption records do not have an associated active connection and/or destination.
- As a result this table may now contain additional records that were previously excluded.
- For more details, see the corresponding DECISIONLOG entry.

Updated package to maintain compatibility with dbt-core versions both before and after v1.10.6, which introduced a breaking change to multi-argument test syntax (e.g., unique_combination_of_columns).
Temporarily removed unsupported tests to avoid errors and ensure smoother upgrades across different dbt-core versions. These tests will be reintroduced once a safe migration path is available.
- Removed all dbt_utils.unique_combination_of_columns tests.
- Moved loaded_at_field: _fivetran_synced under the config: block in src_fivetran_log.yml.

Updated the is_incremental_compatible() macro to include Databricks SQL Warehouses.
Introduced a new macro, is_databricks_all_purpose_cluster(), to distinguish between Databricks All Purpose Clusters and SQL Warehouses.
Updated conditions in .github/workflows/auto-release.yml.
Added .github/workflows/generate-docs.yml.

Full Changelog: v2.2.2...v2.3.0