diff --git a/CHANGELOG.md b/CHANGELOG.md
index 8468444f7080..6c1cf336bcbd 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -45,6 +45,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed `NaN` handling in `SQLDatabase` ([#8479](https://github.com/pyg-team/pytorch_geometric/pull/8479))
 - Fixed `CaptumExplainer` in case no `index` is passed ([#8440](https://github.com/pyg-team/pytorch_geometric/pull/8440))
 - Fixed `edge_index` construction in the `UPFD` dataset ([#8413](https://github.com/pyg-team/pytorch_geometric/pull/8413))
 - Fixed TorchScript support in `AttentionalAggregation` and `DeepSetsAggregation` ([#8406](https://github.com/pyg-team/pytorch_geometric/pull/8406))
diff --git a/test/data/test_database.py b/test/data/test_database.py
index 5d7d055fb19a..e2b8131ff716 100644
--- a/test/data/test_database.py
+++ b/test/data/test_database.py
@@ -1,3 +1,4 @@
+import math
 import os.path as osp
 
 import pytest
@@ -17,7 +18,7 @@
 
 @pytest.mark.parametrize('Database', AVAILABLE_DATABASES)
 @pytest.mark.parametrize('batch_size', [None, 1])
-def test_databases_single_tensor(tmp_path, Database, batch_size):
+def test_database_single_tensor(tmp_path, Database, batch_size):
     kwargs = dict(path=osp.join(tmp_path, 'storage.db'))
     if Database == SQLiteDatabase:
         kwargs['name'] = 'test_table'
@@ -56,7 +57,7 @@ def test_databases_single_tensor(tmp_path, Database, batch_size):
 
 
 @pytest.mark.parametrize('Database', AVAILABLE_DATABASES)
-def test_databases_schema(tmp_path, Database):
+def test_database_schema(tmp_path, Database):
     kwargs = dict(name='test_table') if Database == SQLiteDatabase else {}
 
     path = osp.join(tmp_path, 'tuple_storage.db')
@@ -70,9 +71,9 @@ def test_databases_schema(tmp_path, Database):
         4: object,
     }
 
-    data1 = (1, 0.1, 'a', torch.randn(2, 8), Data(x=torch.randn(1, 8)))
-    data2 = (2, 0.2, 'b', torch.randn(2, 16), Data(x=torch.randn(2, 8)))
-    data3 = (3, 0.3, 'c', torch.randn(2, 32), Data(x=torch.randn(3, 8)))
+    data1 = (1, 0.1, 'a', torch.randn(2, 8), Data(x=torch.randn(8)))
+    data2 = (2, float('inf'), 'b', torch.randn(2, 16), Data(x=torch.randn(8)))
+    data3 = (3, float('NaN'), 'c', torch.randn(2, 32), Data(x=torch.randn(8)))
     db.insert(0, data1)
     db.multi_insert([1, 2], [data2, data3])
 
@@ -81,7 +82,10 @@ def test_databases_schema(tmp_path, Database):
 
     for out, data in zip([out1, out2, out3], [data1, data2, data3]):
         assert out[0] == data[0]
-        assert out[1] == data[1]
+        if math.isnan(data[1]):
+            assert math.isnan(out[1])
+        else:
+            assert out[1] == data[1]
         assert out[2] == data[2]
         assert torch.equal(out[3], data[3])
         assert isinstance(out[4], Data) and len(out[4]) == 1
diff --git a/torch_geometric/data/database.py b/torch_geometric/data/database.py
index 2d4912171e1e..ec5850945ec4 100644
--- a/torch_geometric/data/database.py
+++ b/torch_geometric/data/database.py
@@ -284,8 +284,8 @@ def __init__(self, path: str, name: str, schema: Schema = object):
         # Create the table (if it does not exist) by mapping the Python schema
         # to the corresponding SQL schema:
         sql_schema = ',\n'.join([
-            f'  {col_name} {self._to_sql_type(type_info)} NOT NULL' for
-            col_name, type_info in zip(self._col_names, self.schema.values())
+            f'  {col_name} {self._to_sql_type(type_info)}' for col_name,
+            type_info in zip(self._col_names, self.schema.values())
         ])
         query = (f'CREATE TABLE IF NOT EXISTS {self.name} (\n'
                  f'  id INTEGER PRIMARY KEY,\n'
@@ -409,13 +409,13 @@ def _dummies(self) -> str:
 
     def _to_sql_type(self, type_info: Any) -> str:
         if type_info == int:
-            return 'INTEGER'
+            return 'INTEGER NOT NULL'
         if type_info == float:
             return 'FLOAT'
         if type_info == str:
-            return 'TEXT'
+            return 'TEXT NOT NULL'
         else:
-            return 'BLOB'
+            return 'BLOB NOT NULL'
 
     def _serialize(self, row: Any) -> List[Any]:
         # Serializes the given input data according to `schema`:
@@ -455,7 +455,9 @@ def _deserialize(self, row: Tuple[Any]) -> Any:
                 else:
                     tensor = torch.empty(0, dtype=col_schema.dtype)
                 out_dict[key] = tensor.view(*col_schema.size)
-            elif col_schema in {int, float, str}:
+            elif col_schema == float:
+                out_dict[key] = value if value is not None else float('NaN')
+            elif col_schema in {int, str}:
                 out_dict[key] = value
             else:
                 out_dict[key] = pickle.loads(value)