fix: override is_string/is_number/is_float/is_integer on DatabricksColumn

The base Column class type classification methods don't recognize
Spark/Databricks type names like 'string', 'int', 'bigint', 'double'.
This causes col.is_string() to return False for string columns and
col.is_number() to return False for numeric columns.

Override these methods on DatabricksColumn with the complete set of
Databricks/Spark type names.

Closes #1380

Author: psaikaushik

dbt/adapters/databricks/column.py

@@ -28,6 +28,53 @@ def create(cls, name: str, label_or_dtype: str) -> "DatabricksColumn":
         column_type = cls.translate_type(label_or_dtype)
         return cls(name, column_type)
 
+    def is_string(self) -> bool:
+        return self.dtype.lower() in {
+            "string",
+            "varchar",
+            "char",
+            "text",
+            "character varying",
+            "character",
+            "nchar",
+            "nvarchar",
+        }
+
+    def is_number(self) -> bool:
+        return self.dtype.lower() in {
+            "tinyint",
+            "smallint",
+            "int",
+            "integer",
+            "bigint",
+            "long",
+            "float",
+            "double",
+            "decimal",
+            "numeric",
+            "real",
+        } or self.dtype.lower().startswith("decimal(")
+
+    def is_float(self) -> bool:
+        return self.dtype.lower() in {
+            "float",
+            "double",
+            "real",
+        }
+
+    def is_integer(self) -> bool:
+        return self.dtype.lower() in {
+            "tinyint",
+            "smallint",
+            "int",
+            "integer",
+            "bigint",
+            "long",
+        }
+
+    def is_numeric(self) -> bool:
+        return self.is_number()
+
     @classmethod
     def from_json_metadata(cls, json_metadata: str) -> list["DatabricksColumn"]:
         """