diff --git a/paimon-python/pypaimon/tests/reader_primary_key_test.py b/paimon-python/pypaimon/tests/reader_primary_key_test.py
index bcbc94bd68e7..75bd5a81da88 100644
--- a/paimon-python/pypaimon/tests/reader_primary_key_test.py
+++ b/paimon-python/pypaimon/tests/reader_primary_key_test.py
@@ -141,12 +141,12 @@ def test_pk_multi_write_once_commit(self):
 
         read_builder = table.new_read_builder()
         actual = self._read_test_table(read_builder).sort_by('user_id')
-        # TODO support pk merge feature when multiple write
+        # Primary key deduplication keeps the latest record for each primary key
         expected = pa.Table.from_pydict({
-            'user_id': [1, 2, 2, 3, 4, 5, 7, 8],
-            'item_id': [1001, 1002, 1002, 1003, 1004, 1005, 1007, 1008],
-            'behavior': ['a', 'b', 'b-new', 'c', None, 'e', 'g', 'h'],
-            'dt': ['p1', 'p1', 'p1', 'p2', 'p1', 'p2', 'p1', 'p2'],
+            'user_id': [1, 2, 3, 4, 5, 7, 8],
+            'item_id': [1001, 1002, 1003, 1004, 1005, 1007, 1008],
+            'behavior': ['a', 'b-new', 'c', None, 'e', 'g', 'h'],
+            'dt': ['p1', 'p1', 'p2', 'p1', 'p2', 'p1', 'p2'],
         }, schema=self.pa_schema)
         self.assertEqual(actual, expected)
 
diff --git a/paimon-python/pypaimon/write/writer/key_value_data_writer.py b/paimon-python/pypaimon/write/writer/key_value_data_writer.py
index 05cad9bca95e..dae9ceed16bb 100644
--- a/paimon-python/pypaimon/write/writer/key_value_data_writer.py
+++ b/paimon-python/pypaimon/write/writer/key_value_data_writer.py
@@ -30,8 +30,24 @@ def _process_data(self, data: pa.RecordBatch) -> pa.Table:
         return pa.Table.from_batches([self._sort_by_primary_key(enhanced_data)])
 
     def _merge_data(self, existing_data: pa.Table, new_data: pa.Table) -> pa.Table:
+        """Merge existing data with new data and deduplicate by primary key.
+
+        The merge process:
+        1. Concatenate existing and new data
+        2. Sort by primary key fields and sequence number
+        3. Deduplicate by primary key, keeping the record with maximum sequence number
+
+        Args:
+            existing_data: Previously buffered data
+            new_data: Newly written data to be merged
+
+        Returns:
+            Deduplicated and sorted table
+        """
         combined = pa.concat_tables([existing_data, new_data])
-        return self._sort_by_primary_key(combined)
+        sorted_data = self._sort_by_primary_key(combined)
+        deduplicated_data = self._deduplicate_by_primary_key(sorted_data)
+        return deduplicated_data
 
     def _add_system_fields(self, data: pa.RecordBatch) -> pa.RecordBatch:
         """Add system fields: _KEY_{pk_key}, _SEQUENCE_NUMBER, _VALUE_KIND."""
@@ -53,7 +69,58 @@ def _add_system_fields(self, data: pa.RecordBatch) -> pa.RecordBatch:
 
         return enhanced_table
 
+    def _deduplicate_by_primary_key(self, data: pa.RecordBatch) -> pa.RecordBatch:
+        """Deduplicate data by primary key, keeping the record with maximum sequence number.
+
+        Prerequisite: data is sorted by (primary_keys, _SEQUENCE_NUMBER)
+
+        Algorithm: Since data is sorted by primary key and then by sequence number in ascending
+        order, for each primary key group, the last occurrence has the maximum sequence number.
+        We iterate through and track the last index of each primary key, then keep only those rows.
+
+        Args:
+            data: Sorted record batch with system fields (_KEY_*, _SEQUENCE_NUMBER, _VALUE_KIND)
+
+        Returns:
+            Deduplicated record batch with only the latest record per primary key
+        """
+        if data.num_rows <= 1:
+            return data
+
+        # Build primary key column names (prefixed with _KEY_)
+        pk_columns = [f'_KEY_{pk}' for pk in self.trimmed_primary_keys]
+
+        # First pass: find the last index for each primary key
+        last_index_for_key = {}
+        for i in range(data.num_rows):
+            current_key = tuple(
+                data.column(col)[i].as_py() for col in pk_columns
+            )
+            last_index_for_key[current_key] = i
+
+        # Second pass: collect indices to keep (maintaining original order)
+        indices_to_keep = []
+        for i in range(data.num_rows):
+            current_key = tuple(
+                data.column(col)[i].as_py() for col in pk_columns
+            )
+            # Only keep this row if it's the last occurrence of this primary key
+            if i == last_index_for_key[current_key]:
+                indices_to_keep.append(i)
+
+        # Extract kept rows using PyArrow's take operation
+        indices_array = pa.array(indices_to_keep, type=pa.int64())
+        return data.take(indices_array)
+
     def _sort_by_primary_key(self, data: pa.RecordBatch) -> pa.RecordBatch:
+        """Sort data by primary key fields and sequence number.
+
+        Args:
+            data: Record batch to sort
+
+        Returns:
+            Sorted record batch
+        """
         sort_keys = [(key, 'ascending') for key in self.trimmed_primary_keys]
         if '_SEQUENCE_NUMBER' in data.column_names:
             sort_keys.append(('_SEQUENCE_NUMBER', 'ascending'))