diff --git a/dataframely/_plugin.py b/dataframely/_plugin.py
index 7616af3..95486dd 100644
--- a/dataframely/_plugin.py
+++ b/dataframely/_plugin.py
@@ -30,6 +30,7 @@ def all_rules_horizontal(rules: IntoExpr | Iterable[IntoExpr]) -> pl.Expr:
         function_name="all_rules_horizontal",
         args=rules,
         use_abs_path=True,
+        is_elementwise=True,
     )
 
 
@@ -61,9 +62,14 @@ def all_rules_required(
 ) -> pl.Expr:
     """Execute :mod:`~polars.all_horizontal` and `.all` for a set of rules.
 
-    Contrary to :meth:`all_rules`, this method raises a
-    :mod:`~polars.exceptions.ComputeError` at execution time if any rule indicates a
-    validation failure. The `ComputeError` includes a helpful error message.
+    This method differs from :meth:`all_rules` in two ways:
+
+    - It raises a :mod:`~polars.exceptions.ComputeError` at execution time if any
+      rule indicates a validation failure. The `ComputeError` includes a helpful error
+      message.
+    - It broadcasts the resulting boolean series to the length of the input. This allows
+      element-wise evaluation and making this a non-blocking operation on the streaming
+      engine.
 
     Args:
         rules: The rules to evaluate.
@@ -80,5 +86,10 @@ def all_rules_required(
         args=rules,
         kwargs={"null_is_valid": null_is_valid, "schema_name": schema_name},
         use_abs_path=True,
-        returns_scalar=True,
+        # NOTE: Conceptually, we're reducing the input to a single boolean value here.
+        #  However, we set this option to ensure that the plugin does not become
+        #  blocking on the streaming engine. A single boolean value is simply
+        #  broadcast and we're indifferent to actually finding all validation failures
+        #  during `validate` (and simply fail-fast).
+        is_elementwise=True,
     )
diff --git a/dataframely/schema.py b/dataframely/schema.py
index 0836974..213925f 100644
--- a/dataframely/schema.py
+++ b/dataframely/schema.py
@@ -549,6 +549,12 @@ def validate(
                 should raise upon failure. If `False`, the returned lazy frame will
                 fail to collect if the validation does not pass.
 
+                Note:
+                    If running on the streaming engine, lazy validation will potentially
+                    not surface *all* validation issues as the validation is aborted
+                    once the first failure is encountered. Likewise, the reported
+                    validation failure can be non-deterministic.
+
         Returns:
             The input eager or lazy frame, wrapped in a generic version of the
             input's data frame type to reflect schema adherence. Columns not defined
diff --git a/src/polars_plugin/mod.rs b/src/polars_plugin/mod.rs
index 06cb9b9..c42d4f1 100644
--- a/src/polars_plugin/mod.rs
+++ b/src/polars_plugin/mod.rs
@@ -81,7 +81,8 @@ pub fn all_rules_required(
     // neither actually runs the filter logic, nor does it copy any data. It's essentially a no-op
     // that is not optimized away in a lazy frame.
     if failures.is_empty() {
-        return Ok(BooleanChunked::new(PlSmallStr::EMPTY, [true]).into_series());
+        let column = Column::new_scalar(PlSmallStr::EMPTY, Scalar::from(true), 1);
+        return Ok(column.take_materialized_series());
     }
 
     // Aggregate failure counts into a validation error.
diff --git a/tests/schema/test_validate.py b/tests/schema/test_validate.py
index 76af480..7ff383b 100644
--- a/tests/schema/test_validate.py
+++ b/tests/schema/test_validate.py
@@ -115,7 +115,7 @@ def test_invalid_column_contents(
     df = df_type({"a": [1, 2, 3], "b": ["x", "longtext", None], "c": ["1", None, "3"]})
     with pytest.raises(
         ValidationError if eager else plexc.ComputeError,
-        match=r"2 rules failed validation",
+        match=r"2 rules failed validation" if eager else None,
     ):
         _validate_and_collect(MySchema, df, eager=eager)
     assert not MySchema.is_valid(df)
@@ -143,7 +143,7 @@ def test_violated_custom_rule(
     df = df_type({"a": [1, 1, 2, 3, 3], "b": [2, 2, 2, 4, 5]})
     with pytest.raises(
         ValidationError if eager else plexc.ComputeError,
-        match=r"2 rules failed validation",
+        match=r"2 rules failed validation" if eager else None,
     ):
         _validate_and_collect(MyComplexSchema, df, eager=eager)
     assert not MyComplexSchema.is_valid(df)
@@ -285,7 +285,19 @@ def test_multiple_unique_columns_both_invalid(
     df = df_type({"a": [1, 1, 3], "b": ["x", "y", "y"]})
     with pytest.raises(
         ValidationError if eager else plexc.ComputeError,
-        match=r"2 rules failed validation",
+        match=r"2 rules failed validation" if eager else None,
     ):
         _validate_and_collect(MultiUniqueSchema, df, eager=eager)
     assert not MultiUniqueSchema.is_valid(df)
+
+
+# ----------------------------------- PERFORMANCE ------------------------------------ #
+
+
+def test_lazy_validate_does_not_block_streaming_engine() -> None:
+    schema = create_schema("test", {"a": dy.Int64(), "b": dy.Int64()})
+    lf = pl.LazyFrame({"a": [1, 2, 3], "b": [2, 3, 4]}).lazy()
+    out = schema.validate(lf, eager=False)
+    graph = out.show_graph(engine="streaming", plan_stage="physical", raw_output=True)
+    assert graph is not None
+    assert "in-memory-map" not in graph