diff --git a/duckdb/experimental/spark/sql/dataframe.py b/duckdb/experimental/spark/sql/dataframe.py
index 83b2dd09..dcedc0b9 100644
--- a/duckdb/experimental/spark/sql/dataframe.py
+++ b/duckdb/experimental/spark/sql/dataframe.py
@@ -1,5 +1,6 @@
+import itertools
 import uuid
-from collections.abc import Callable
+from collections.abc import Callable, Iterable, Iterator
 from functools import reduce
 from keyword import iskeyword
 from typing import (
@@ -19,6 +20,8 @@
 from .type_utils import duckdb_to_spark_schema
 from .types import Row, StructType
 
+_LOCAL_ITERATOR_BATCH_SIZE = 10_000
+
 if TYPE_CHECKING:
     import pyarrow as pa
     from pandas.core.frame import DataFrame as PandasDataFrame
@@ -27,7 +30,13 @@
     from .group import GroupedData
     from .session import SparkSession
 
-from duckdb.experimental.spark.sql import functions as spark_sql_functions
+from duckdb.experimental.spark.sql import functions as spark_sql_functions  # noqa: E402
+
+
+def _construct_row(values: Iterable, names: list[str]) -> Row:
+    row = tuple.__new__(Row, list(values))
+    row.__fields__ = list(names)
+    return row
 
 
 class DataFrame:  # noqa: D101
@@ -70,6 +79,149 @@ def toArrow(self) -> "pa.Table":
         """
         return self.relation.to_arrow_table()
 
+    def toLocalIterator(self, prefetchPartitions: bool = False) -> Iterator[Row]:
+        """Returns an iterator that contains all of the rows in this :class:`DataFrame`.
+
+        The iterator will consume as much memory as the largest partition in this
+        :class:`DataFrame`. With prefetch it may consume up to the memory of the 2 largest
+        partitions.
+
+        .. versionadded:: 2.0.0
+
+        .. versionchanged:: 3.4.0
+            Supports Spark Connect.
+
+        Parameters
+        ----------
+        prefetchPartitions : bool, optional
+            If Spark should pre-fetch the next partition before it is needed.
+
+            .. versionchanged:: 3.4.0
+                This argument does not take effect for Spark Connect.
+
+        Returns:
+        -------
+        Iterator
+            Iterator of rows.
+
+        Examples:
+        --------
+        >>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
+        >>> list(df.toLocalIterator())
+        [Row(age=14, name='Tom'), Row(age=23, name='Alice'), Row(age=16, name='Bob')]
+        """
+        columns = self.relation.columns
+        cur = self.relation.execute()
+
+        try:
+            while rows := cur.fetchmany(_LOCAL_ITERATOR_BATCH_SIZE):
+                yield from (_construct_row(x, columns) for x in rows)
+        finally:
+            cur.close()
+
+    def foreach(self, f: Callable[[Row], None]) -> None:
+        """Applies the ``f`` function to all :class:`Row` of this :class:`DataFrame`.
+
+        This is a shorthand for ``df.rdd.foreach()``.
+
+        .. versionadded:: 1.3.0
+
+        .. versionchanged:: 4.0.0
+            Supports Spark Connect.
+
+        Parameters
+        ----------
+        f : function
+            A function that accepts one parameter which will
+            receive each row to process.
+
+        Examples:
+        --------
+        >>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
+        >>> def func(person):
+        ...     print(person.name)
+        >>> df.foreach(func)
+        """
+        for row in self.toLocalIterator():
+            f(row)
+
+    def foreachPartition(self, f: Callable[[Iterator[Row]], None]) -> None:
+        """Applies the ``f`` function to each partition of this :class:`DataFrame`.
+
+        This a shorthand for ``df.rdd.foreachPartition()``.
+
+        .. versionadded:: 1.3.0
+
+        .. versionchanged:: 4.0.0
+            Supports Spark Connect.
+
+        Parameters
+        ----------
+        f : function
+            A function that accepts one parameter which will receive
+            each partition to process.
+
+        Examples:
+        --------
+        >>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
+        >>> def func(itr):
+        ...     for person in itr:
+        ...         print(person.name)
+        >>> df.foreachPartition(func)
+        """
+        rows_generator = self.toLocalIterator()
+        while rows := itertools.islice(rows_generator, _LOCAL_ITERATOR_BATCH_SIZE):
+            f(iter(rows))
+
+    def isEmpty(self) -> bool:
+        """Checks if the :class:`DataFrame` is empty and returns a boolean value.
+
+        .. versionadded:: 3.3.0
+
+        .. versionchanged:: 3.4.0
+            Supports Spark Connect.
+
+        Returns:
+        -------
+        bool
+            Returns ``True`` if the DataFrame is empty, ``False`` otherwise.
+
+        See Also:
+        --------
+        DataFrame.count : Counts the number of rows in DataFrame.
+
+        Notes:
+        -----
+        - An empty DataFrame has no rows. It may have columns, but no data.
+
+        Examples:
+        --------
+        Example 1: Checking if an empty DataFrame is empty
+
+        >>> df_empty = spark.createDataFrame([], "a STRING")
+        >>> df_empty.isEmpty()
+        True
+
+        Example 2: Checking if a non-empty DataFrame is empty
+
+        >>> df_non_empty = spark.createDataFrame(["a"], "STRING")
+        >>> df_non_empty.isEmpty()
+        False
+
+        Example 3: Checking if a DataFrame with null values is empty
+
+        >>> df_nulls = spark.createDataFrame([(None, None)], "a STRING, b INT")
+        >>> df_nulls.isEmpty()
+        False
+
+        Example 4: Checking if a DataFrame with no rows but with columns is empty
+
+        >>> df_no_rows = spark.createDataFrame([], "id INT, value STRING")
+        >>> df_no_rows.isEmpty()
+        True
+        """
+        return self.first() is None
+
     def createOrReplaceTempView(self, name: str) -> None:
         """Creates or replaces a local temporary view with this :class:`DataFrame`.
 
@@ -1392,12 +1544,7 @@ def collect(self) -> list[Row]:  # noqa: D102
         columns = self.relation.columns
         result = self.relation.fetchall()
 
-        def construct_row(values: list, names: list[str]) -> Row:
-            row = tuple.__new__(Row, list(values))
-            row.__fields__ = list(names)
-            return row
-
-        rows = [construct_row(x, columns) for x in result]
+        rows = [_construct_row(x, columns) for x in result]
         return rows
 
     def cache(self) -> "DataFrame":
diff --git a/external/duckdb b/external/duckdb
index 376cb731..2e305aac 160000
--- a/external/duckdb
+++ b/external/duckdb
@@ -1 +1 @@
-Subproject commit 376cb731d34122262308eaf3ecc7781862f8eab5
+Subproject commit 2e305aac809ef22e818024be1e86a1d0ee0d2863
diff --git a/tests/fast/spark/test_spark_dataframe.py b/tests/fast/spark/test_spark_dataframe.py
index e242092e..19e99921 100644
--- a/tests/fast/spark/test_spark_dataframe.py
+++ b/tests/fast/spark/test_spark_dataframe.py
@@ -1,3 +1,5 @@
+from unittest import mock
+
 import pytest
 
 _ = pytest.importorskip("duckdb.experimental.spark")
@@ -597,3 +599,40 @@ def test_treeString_array_type(self, spark):
         assert " |-- name:" in tree
         assert " |-- hobbies: array<" in tree
         assert "(nullable = true)" in tree
+
+    def test_method_is_empty(self, spark):
+        data = [(1, "Alice"), (2, "Bob")]
+        df = spark.createDataFrame(data, ["id", "name"])
+        empty_df = spark.createDataFrame([], schema=df.schema)
+
+        assert not df.isEmpty()
+        assert empty_df.isEmpty()
+
+    def test_dataframe_foreach(self, spark):
+        data = [(56, "Carol"), (20, "Alice"), (3, "Dave")]
+        df = spark.createDataFrame(data, ["age", "name"])
+        expected = [Row(age=56, name="Carol"), Row(age=20, name="Alice"), Row(age=3, name="Dave")]
+
+        mock_callable = mock.MagicMock()
+        df.foreach(mock_callable)
+        mock_callable.assert_has_calls(
+            [mock.call(expected[0]), mock.call(expected[1]), mock.call(expected[2])],
+            any_order=True,
+        )
+
+    def test_dataframe_foreach_partition(self, spark):
+        data = [(56, "Carol"), (20, "Alice"), (3, "Dave")]
+        df = spark.createDataFrame(data, ["age", "name"])
+        expected = [Row(age=56, name="Carol"), Row(age=20, name="Alice"), Row(age=3, name="Dave")]
+
+        mock_callable = mock.MagicMock()
+        df.foreachPartition(mock_callable)
+        mock_callable.assert_called_once_with(expected)
+
+    def test_to_local_iterator(self, spark):
+        data = [(56, "Carol"), (20, "Alice"), (3, "Dave")]
+        df = spark.createDataFrame(data, ["age", "name"])
+        expected = [Row(age=56, name="Carol"), Row(age=20, name="Alice"), Row(age=3, name="Dave")]
+
+        res = list(df.toLocalIterator())
+        assert res == expected