v1.8.0 dbt_fivetran_log
fivetran-data-model-bot
released this
12 Jun 13:46
·
3 commits
to main
since this release
PR #130 includes the following updates:
🚨 Breaking Changes 🚨
⚠️ Since the following changes result in the table format changing, we recommend running a--full-refresh
after upgrading to this version to avoid possible incremental failures.
- For Databricks All-Purpose clusters, the
fivetran_platform__audit_table
model will now be materialized using the delta table format (previously parquet).- Delta tables are generally more performant than parquet and are also more widely available for Databricks users. Previously, the parquet file format was causing compilation issues on customers' managed tables.
Documentation Updates
- Updated the
sync_start
andsync_end
field descriptions for thefivetran_platform__audit_table
to explicitly define that these fields only represent the sync start/end times for when the connector wrote new or modified existing records to the specified table. - Addition of integrity and consistency validation tests within integration tests for every end model.
- Removed duplicate Databricks dispatch instructions listed in the README.
Under the Hood
- The
is_databricks_sql_warehouse
macro has been renamed tois_incremental_compatible
and has been modified to returntrue
if the Databricks runtime being used is an all-purpose cluster (previously this macro checked if a sql warehouse runtime was used) or if any other non-Databricks supported destination is being used.- This update was applied as there have been other Databricks runtimes discovered (ie. an endpoint and external runtime) which do not support the
insert_overwrite
incremental strategy used in thefivetran_platform__audit_table
model.
- This update was applied as there have been other Databricks runtimes discovered (ie. an endpoint and external runtime) which do not support the
- In addition to the above, for Databricks users the
fivetran_platform__audit_table
model will now leverage the incremental strategy only if the Databricks runtime is all-purpose. Otherwise, all other Databricks runtimes will not leverage an incremental strategy.
Full Changelog: v1.7.3...v1.8.0