diff --git a/pandasai/data_loader/loader.py b/pandasai/data_loader/loader.py
index d7a133320..9612e3c17 100644
--- a/pandasai/data_loader/loader.py
+++ b/pandasai/data_loader/loader.py
@@ -28,13 +28,14 @@ def load(self, dataset_path: str, virtualized=False) -> DataFrame:
             cache_file = self._get_cache_file_path()
 
             if self._is_cache_valid(cache_file):
-                return self._read_cache(cache_file)
+                cache_format = self.schema["destination"]["format"]
+                return self._read_csv_or_parquet(cache_file, cache_format)
 
             df = self._load_from_source()
             df = self._apply_transformations(df)
             self._cache_data(df, cache_file)
 
-            table_name = self.schema["source"]["table"]
+            table_name = self.schema["source"].get("table", None) or self.schema["name"]
             table_description = self.schema.get("description", None)
 
             return DataFrame(
@@ -47,7 +48,7 @@ def load(self, dataset_path: str, virtualized=False) -> DataFrame:
         else:
             # Initialize new dataset loader for virtualization
             data_loader = self.copy()
-            table_name = self.schema["source"]["table"]
+            table_name = self.schema["source"].get("table", None) or self.schema["name"]
             table_description = self.schema.get("description", None)
             return VirtualDataFrame(
                 schema=self.schema,
@@ -69,7 +70,10 @@ def _load_schema(self):
 
     def _validate_source_type(self):
         source_type = self.schema["source"]["type"]
-        if source_type not in SUPPORTED_SOURCES:
+        if source_type not in SUPPORTED_SOURCES and source_type not in [
+            "csv",
+            "parquet",
+        ]:
             raise ValueError(f"Unsupported database type: {source_type}")
 
     def _get_cache_file_path(self) -> str:
@@ -88,36 +92,13 @@ def _is_cache_valid(self, cache_file: str) -> bool:
             return False
 
         file_mtime = datetime.fromtimestamp(os.path.getmtime(cache_file))
-        update_frequency = self.schema["update_frequency"]
+        update_frequency = self.schema.get("update_frequency", None)
 
-        if update_frequency == "weekly":
+        if update_frequency and update_frequency == "weekly":
             return file_mtime > datetime.now() - timedelta(weeks=1)
 
         return False
 
-    def _read_cache(self, cache_file: str) -> DataFrame:
-        cache_format = self.schema["destination"]["format"]
-        table_name = self.schema["source"]["table"]
-        table_description = self.schema.get("description", None)
-        if cache_format == "parquet":
-            return DataFrame(
-                pd.read_parquet(cache_file),
-                schema=self.schema,
-                path=self.dataset_path,
-                name=table_name,
-                description=table_description,
-            )
-        elif cache_format == "csv":
-            return DataFrame(
-                pd.read_csv(cache_file),
-                schema=self.schema,
-                path=self.dataset_path,
-                name=table_name,
-                description=table_description,
-            )
-        else:
-            raise ValueError(f"Unsupported cache format: {cache_format}")
-
     def _get_loader_function(self, source_type: str):
         """
         Get the loader function for a specified data source type.
@@ -148,7 +129,36 @@ def _get_loader_function(self, source_type: str):
                 f"Please install the {SUPPORTED_SOURCES[source_type]} library."
             ) from e
 
+    def _read_csv_or_parquet(self, file_path: str, format: str) -> DataFrame:
+        table_name = self.schema["source"].get("table") or self.schema.get("name", None)
+        table_description = self.schema.get("description", None)
+        if format == "parquet":
+            return DataFrame(
+                pd.read_parquet(file_path),
+                schema=self.schema,
+                path=self.dataset_path,
+                name=table_name,
+                description=table_description,
+            )
+        elif format == "csv":
+            return DataFrame(
+                pd.read_csv(file_path),
+                schema=self.schema,
+                path=self.dataset_path,
+                name=table_name,
+                description=table_description,
+            )
+        else:
+            raise ValueError(f"Unsupported file format: {format}")
+
     def _load_from_source(self) -> pd.DataFrame:
+        source_type = self.schema["source"]["type"]
+        if source_type in ["csv", "parquet"]:
+            filpath = os.path.join(
+                "datasets", self.dataset_path, self.schema["source"]["path"]
+            )
+            return self._read_csv_or_parquet(filpath, source_type)
+
         query_builder = QueryBuilder(self.schema)
         query = query_builder.build_query()
         return self.execute_query(query)
diff --git a/pandasai/dataframe/base.py b/pandasai/dataframe/base.py
index e5a40957d..12ce616e3 100644
--- a/pandasai/dataframe/base.py
+++ b/pandasai/dataframe/base.py
@@ -166,15 +166,18 @@ def _create_yml_template(self, name, description, columns: List[dict]):
             table_name (str): Name of the table or dataset.
         """
         # Metadata template
-        metadata = {
+        return {
             "name": name,
             "description": description,
             "columns": columns,
             "source": {"type": "parquet", "path": "data.parquet"},
+            "destination": {
+                "type": "local",
+                "format": "parquet",
+                "path": "data.parquet",
+            },
         }
 
-        return metadata
-
     def save(
         self, path: str, name: str, description: str = None, columns: List[dict] = []
     ):