test: add unit tests and changelog for type classification fix (#1380)

Address review feedback: add parametrized unit tests covering all five
overridden methods (is_string, is_integer, is_float, is_number,
is_numeric) with true/false cases and case-insensitive checks, and
add a CHANGELOG entry under 1.11.7.

https://claude.ai/code/session_017ZAXMwLSnqz4FqvTt5H7D1

Author: psaikaushik

CHANGELOG.md

@@ -10,6 +10,7 @@
 - Fix `workflow_job` Python model submission method failing with dictionary attribute error ([#1360](https://github.com/databricks/dbt-databricks/issues/1360))
 - Fix `TestWorkflowJob` functional test that was unreachable on all profiles due to incorrect skip list, wrong model fixture, and invalid `max_retries` parameter ([#1360](https://github.com/databricks/dbt-databricks/issues/1360))
 - Fix column order mismatch in microbatch and replace_where incremental strategies by using INSERT BY NAME syntax ([#1338](https://github.com/databricks/dbt-databricks/issues/1338))
+- Fix `is_string()`, `is_number()`, `is_float()`, `is_integer()`, and `is_numeric()` returning `False` for Databricks/Spark column types by overriding them in `DatabricksColumn` with the correct Spark type names ([#1380](https://github.com/databricks/dbt-databricks/issues/1380))
 - Fix `dbt run --empty` failing with inline `ref()` / `source()` aliases ([dbt-labs/dbt-adapters#660](https://github.com/dbt-labs/dbt-adapters/issues/660))
 
 ### Under the Hood

tests/unit/test_column.py

@@ -290,3 +290,56 @@ def test_parse_type_from_json_primitive_types(self):
             type_info = {"name": type_name}
             result = DatabricksColumn._parse_type_from_json(type_info)
             assert result == type_name
+
+
+class TestTypeClassification:
+    @pytest.mark.parametrize(
+        "dtype",
+        ["string", "varchar", "char", "text", "character varying", "character", "nchar", "nvarchar",
+         "STRING", "VARCHAR", "CHAR"],
+    )
+    def test_is_string_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_string() is True
+
+    @pytest.mark.parametrize("dtype", ["int", "bigint", "double", "decimal(10,2)"])
+    def test_is_string_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_string() is False
+
+    @pytest.mark.parametrize(
+        "dtype",
+        ["tinyint", "smallint", "int", "integer", "bigint", "long", "INT", "BIGINT"],
+    )
+    def test_is_integer_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_integer() is True
+
+    @pytest.mark.parametrize("dtype", ["float", "double", "string", "decimal(10,2)"])
+    def test_is_integer_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_integer() is False
+
+    @pytest.mark.parametrize("dtype", ["float", "double", "real", "FLOAT", "DOUBLE"])
+    def test_is_float_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_float() is True
+
+    @pytest.mark.parametrize("dtype", ["int", "bigint", "string", "decimal(10,2)"])
+    def test_is_float_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_float() is False
+
+    @pytest.mark.parametrize(
+        "dtype",
+        ["tinyint", "smallint", "int", "integer", "bigint", "long",
+         "float", "double", "decimal", "numeric", "real",
+         "decimal(10,2)", "decimal(38,0)", "DECIMAL(10,2)"],
+    )
+    def test_is_number_true(self, dtype):
+        assert DatabricksColumn("col", dtype).is_number() is True
+
+    @pytest.mark.parametrize("dtype", ["string", "varchar", "boolean", "date", "timestamp"])
+    def test_is_number_false(self, dtype):
+        assert DatabricksColumn("col", dtype).is_number() is False
+
+    def test_is_numeric_delegates_to_is_number(self):
+        col = DatabricksColumn("col", "bigint")
+        assert col.is_numeric() == col.is_number()
+
+    def test_is_numeric_false_for_string(self):
+        assert DatabricksColumn("col", "string").is_numeric() is False