Release Version 1.3.0 · databricks/dbt-databricks

Features

Support python model through run command API, currently supported materializations are table and incremental. (dbt-labs/dbt-spark#377, #126)
Enable Pandas and Pandas-on-Spark DataFrames for dbt python models (dbt-labs/dbt-spark#469, #181)
Support job cluster in notebook submission method (dbt-labs/dbt-spark#467, #194)
- In all_purpose_cluster submission method, a config http_path can be specified in Python model config to switch the cluster where Python model runs.
```
def model(dbt, _):
    dbt.config(
        materialized='table',
        http_path='...'
    )
    ...
```
Use builtin timestampadd and timestampdiff functions for dateadd/datediff macros if available (#185)
Implement testing for a test for various Python models (#189)
Implement testing for type_boolean in Databricks (dbt-labs/dbt-spark#471, #188)
Add a macro to support COPY INTO (#190)

Apply "Initial refactoring of incremental materialization" (#148)
- Now dbt-databricks uses adapter.get_incremental_strategy_macro instead of dbt_spark_get_incremental_sql macro to dispatch the incremental strategy macro. The overwritten dbt_spark_get_incremental_sql macro will not work anymore.
Better interface for python submission (dbt-labs/dbt-spark#452, #178)