feature/databricks-sql-warehouse-compatibility

.buildkite/hooks/pre-command

@@ -23,6 +23,8 @@ export CI_DATABRICKS_DBT_HOST=$(gcloud secrets versions access latest --secret="
 export CI_DATABRICKS_DBT_HTTP_PATH=$(gcloud secrets versions access latest --secret="CI_DATABRICKS_DBT_HTTP_PATH" --project="dbt-package-testing-363917")
 export CI_DATABRICKS_DBT_TOKEN=$(gcloud secrets versions access latest --secret="CI_DATABRICKS_DBT_TOKEN" --project="dbt-package-testing-363917")
 export CI_DATABRICKS_DBT_CATALOG=$(gcloud secrets versions access latest --secret="CI_DATABRICKS_DBT_CATALOG" --project="dbt-package-testing-363917")
+export CI_DATABRICKS_SQL_DBT_HTTP_PATH=$(gcloud secrets versions access latest --secret="CI_DATABRICKS_SQL_DBT_HTTP_PATH" --project="dbt-package-testing-363917")
+export CI_DATABRICKS_SQL_DBT_TOKEN=$(gcloud secrets versions access latest --secret="CI_DATABRICKS_SQL_DBT_TOKEN" --project="dbt-package-testing-363917")
 export CI_SQLSERVER_DBT_SERVER=$(gcloud secrets versions access latest --secret="CI_SQLSERVER_DBT_SERVER" --project="dbt-package-testing-363917")
 export CI_SQLSERVER_DBT_DATABASE=$(gcloud secrets versions access latest --secret="CI_SQLSERVER_DBT_DATABASE" --project="dbt-package-testing-363917")
 export CI_SQLSERVER_DBT_USER=$(gcloud secrets versions access latest --secret="CI_SQLSERVER_DBT_USER" --project="dbt-package-testing-363917")

.buildkite/pipeline.yml

@@ -73,6 +73,21 @@ steps:
     commands: |
       bash .buildkite/scripts/run_models.sh databricks
 
+  - label: ":databricks: :database: Run Tests - Databricks SQL Warehouse"
+    key: "run_dbt_databricks_sql"
+    plugins:
+      - docker#v3.13.0:
+          image: "python:3.8"
+          shell: [ "/bin/bash", "-e", "-c" ]
+          environment:
+            - "BASH_ENV=/tmp/.bashrc"
+            - "CI_DATABRICKS_DBT_HOST"
+            - "CI_DATABRICKS_SQL_DBT_HTTP_PATH"
+            - "CI_DATABRICKS_SQL_DBT_TOKEN"
+            - "CI_DATABRICKS_DBT_CATALOG"
+    commands: |
+      bash .buildkite/scripts/run_models.sh databricks-sql
+
   - label: ":azure: Run Tests - SQLServer"
     key: "run_dbt_sqlserver"
     plugins:

.buildkite/scripts/run_models.sh

@@ -36,6 +36,22 @@ db=$1
 echo `pwd`
 cd integration_tests
 dbt deps
+if [ "$db" = "databricks-sql" ]; then
+dbt seed --vars '{fivetran_platform_schema: sqlw_tests}' --target "$db" --full-refresh
+dbt compile --vars '{fivetran_platform_schema: sqlw_tests}' --target "$db"
+dbt run --vars '{fivetran_platform_schema: sqlw_tests}' --target "$db" --full-refresh
+dbt run --vars '{fivetran_platform_schema: sqlw_tests}' --target "$db"
+dbt test --vars '{fivetran_platform_schema: sqlw_tests}' --target "$db"
+dbt run --vars '{fivetran_platform_schema: sqlw_tests, fivetran_platform__usage_pricing: true}' --target "$db" --full-refresh
+dbt run --vars '{fivetran_platform_schema: sqlw_tests, fivetran_platform__usage_pricing: true}' --target "$db"
+dbt test --target "$db"
+dbt run --vars '{fivetran_platform_schema: sqlw_tests, fivetran_platform__credits_pricing: false, fivetran_platform__usage_pricing: true}' --target "$db" --full-refresh
+dbt run --vars '{fivetran_platform__credits_pricing: false, fivetran_platform__usage_pricing: true}' --target "$db"
+dbt test --vars '{fivetran_platform_schema: sqlw_tests}'  --target "$db"
+dbt run --vars '{fivetran_platform_schema: sqlw_tests, fivetran_platform__usage_pricing: false, fivetran_platform_using_destination_membership: false, fivetran_platform_using_user: false}' --target "$db" --full-refresh
+dbt run --vars '{fivetran_platform_schema: sqlw_tests, fivetran_platform__usage_pricing: false, fivetran_platform_using_destination_membership: false, fivetran_platform_using_user: false}' --target "$db"
+dbt test --vars '{fivetran_platform_schema: sqlw_tests}'  --target "$db"
+else
 dbt seed --target "$db" --full-refresh
 dbt compile --target "$db"
 dbt run --target "$db" --full-refresh
@@ -50,6 +66,7 @@ dbt test --target "$db"
 dbt run --vars '{fivetran_platform__usage_pricing: false, fivetran_platform_using_destination_membership: false, fivetran_platform_using_user: false}' --target "$db" --full-refresh
 dbt run --vars '{fivetran_platform__usage_pricing: false, fivetran_platform_using_destination_membership: false, fivetran_platform_using_user: false}' --target "$db"
 dbt test --target "$db"
+fi
 if [ "$1" != "sqlserver" ]; then
 dbt run-operation fivetran_utils.drop_schemas_automation --target "$db"
 fi

CHANGELOG.md

@@ -1,3 +1,19 @@
+# dbt_fivetran_log v1.7.1
+[PR #121](https://github.com/fivetran/dbt_fivetran_log/pull/121) includes the following updates:
+
+## Bug Fixes
+- Users leveraging the Databricks SQL Warehouse runtime were previously unable to run the `fivetran_platform__audit_table` model due to an incompatible incremental strategy. As such, the following updates have been made:
+  - A new macro `is_databricks_sql_warehouse()` has been added to determine if a databricks runtime is a SQL Warehouse runtime for Databricks. This macro will return a boolean of `true` if the runtime is determined to be SQL Warehouse and `false` if it is any other runtime or destination.
+  - The above macro is used in determining the incremental strategy within the `fivetran_platform__audit_table`. For Databricks SQL Warehouses, there will be **no** incremental strategy used. All other destination runtime strategies are not impacted with this change.
+    - For the SQL Warehouse runtime, the best incremental strategy we could elect to use is the `merge` strategy. However, we do not have full confidence in the resulting data integrity of the output model when leveraging this strategy. Therefore, we opted for the model to replicate a full create or replace behavior for the time being.
+
+## Features
+- Updated README incremental model section to revise descriptions and add information for Databricks SQL Warehouse.
+
+## Under the Hood
+- Added integration testing pipeline for Databricks SQL Warehouse.
+- Applied modifications to the integration testing pipeline to account for jobs being run on both Databricks All Purpose Cluster and SQL Warehouse runtimes.
+
 # dbt_fivetran_log v1.7.0
 [PR #119](https://github.com/fivetran/dbt_fivetran_log/pull/119) includes the following updates:
 

README.md

@@ -53,12 +53,14 @@ dispatch:
 ```
 
 ### Database Incremental Strategies 
-Some of the end models in this package are materialized incrementally. We have chosen `insert_overwrite` as the default strategy for **BigQuery** and **Databricks** databases, as it is only available for these dbt adapters. For **Snowflake**, **Redshift**, and **Postgres** databases, we have chosen `delete+insert` as the default strategy.
+For models in this package that are materialized incrementally, they are configured to work with the different strategies available to each supported warehouse.
 
-`insert_overwrite` is our preferred incremental strategy because it will be able to properly handle updates to records that exist outside the immediate incremental window. That is, because it leverages partitions, `insert_overwrite` will appropriately update existing rows that have been changed upstream instead of inserting duplicates of them--all without requiring a full table scan.
+For **BigQuery** and **Databricks Runtime** destinations, we have chosen `insert_overwrite` as the default strategy, which benefits from the partitioning capability. 
+> For Databricks SQL Warehouse destinations, models are materialized as tables without support for incremental models.
 
-`delete+insert` is our second-choice as it resembles `insert_overwrite` but lacks partitions. This strategy works most of the time and appropriately handles incremental loads that do not contain changes to past records. However, if a past record has been updated and is outside of the incremental window, `delete+insert` will insert a duplicate record. 😱
-> Because of this, we highly recommend that **Snowflake**, **Redshift**, and **Postgres** users periodically run a `--full-refresh` to ensure a high level of data quality and remove any possible duplicates.
+For **Snowflake**, **Redshift**, and **Postgres** databases, we have chosen `delete+insert` as the default strategy.  
+
+> Regardless of strategy, we recommend that users periodically run a `--full-refresh` to ensure a high level of data quality.
 
 ## Step 2: Installing the Package
 Include the following Fivetran Platform package version range in your `packages.yml`

dbt_project.yml

@@ -1,6 +1,6 @@
 config-version: 2
 name: 'fivetran_log'
-version: '1.7.0'
+version: '1.7.1'
 require-dbt-version: [">=1.3.0", "<2.0.0"]
 
 models:

integration_tests/ci/sample.profiles.yml

@@ -52,6 +52,14 @@ integration_tests:
       threads: 2
       token: "{{ env_var('CI_DATABRICKS_DBT_TOKEN') }}"
       type: databricks
+    databricks-sql:
+      catalog: "{{ env_var('CI_DATABRICKS_DBT_CATALOG') }}"
+      host: "{{ env_var('CI_DATABRICKS_DBT_HOST') }}"
+      http_path: "{{ env_var('CI_DATABRICKS_SQL_DBT_HTTP_PATH') }}"
+      schema: sqlw_tests 
+      threads: 2
+      token: "{{ env_var('CI_DATABRICKS_SQL_DBT_TOKEN') }}"
+      type: databricks
     sqlserver:
       type: sqlserver
       driver: 'ODBC Driver 18 for SQL Server'

integration_tests/dbt_project.yml

@@ -1,5 +1,5 @@
 name: 'fivetran_log_integration_tests'
-version: '1.7.0'
+version: '1.7.1'
 
 config-version: 2
 profile: 'integration_tests'
@@ -10,7 +10,7 @@ dispatch:
 
 vars:
   fivetran_log:
-    fivetran_platform_schema: fivetran_platform_integration_tests
+    fivetran_platform_schema: "fivetran_platform_integration_tests"
     fivetran_platform_account_identifier: "account"
     fivetran_platform_incremental_mar_identifier: "incremental_mar"
     fivetran_platform_connector_identifier: "connector"
@@ -24,7 +24,7 @@ vars:
 
 models:
   fivetran_log:
-    +schema: fivetran_platform
+    +schema: "{{ 'sqlw_tests' if target.name == 'databricks-sql' else 'fivetran_platform' }}"
 
 seeds:
   fivetran_log_integration_tests:

macros/is_databricks_sql_warehouse.sql

@@ -0,0 +1,15 @@
+{% macro is_databricks_sql_warehouse(target) %}
+    {% if target.type in ('databricks','spark') %}
+        {% set re = modules.re %}
+        {% set path_match = target.http_path %}
+        {% set regex_pattern = "/sql/.+/warehouses/" %}
+        {% set match_result = re.search(regex_pattern, path_match) %}
+        {% if match_result %}
+            {{ return(True) }}
+        {% else %}
+            {{ return(False) }}
+        {% endif %}
+    {% else %}
+        {{ return(False) }}
+    {% endif %}
+{% endmacro %}

models/fivetran_platform__audit_table.sql

@@ -1,13 +1,13 @@
 {{ config(
-    materialized='incremental',
+    materialized='table' if is_databricks_sql_warehouse(target) else 'incremental',
     unique_key='unique_table_sync_key',
     partition_by={
         'field': 'sync_start_day',
         'data_type': 'date'
     } if target.type == 'bigquery' else ['sync_start_day'],
     cluster_by = ['sync_start_day'],
-    incremental_strategy='insert_overwrite' if target.type in ('bigquery', 'spark', 'databricks') else 'delete+insert',
-    file_format='parquet'
+    incremental_strategy='insert_overwrite' if target.type in ('bigquery','spark', 'databricks') else 'delete+insert',
+    file_format='delta' if is_databricks_sql_warehouse(target) else 'parquet'
 ) }}
 
 with sync_log as (