Document clarifying notes about the data lifecycle

docs/user_guide/concepts/main_concepts/data_management.rst

@@ -170,6 +170,13 @@ But for Metadata, the data should be accessible to Flyte control plane.
 
 Data persistence is also pluggable. By default, it supports all major blob stores and uses an interface defined in Flytestdlib.
 
+Deleting Raw Data in Your Own Datastores
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Flyte does not offer a direct function to delete raw data stored in external datastores like ``S3`` or ``GCS``. However, you can manage deletion by configuring a lifecycle policy within your datastore service.
+
+If caching is enabled in your Flyte ``task``, ensure that the ``max-cache-age`` is set to be shorter than the lifecycle policy in your datastore to prevent potential data inconsistency issues.
+
 Practical Example
 ~~~~~~~~~~~~~~~~~
 
@@ -180,19 +187,18 @@ The first task reads a file from the object store, shuffles the data, saves to l
 .. code-block:: python
 
     @task()
-    def task_remove_column(input_file: FlyteFile, column_name: str) -> FlyteFile:
+    def task_read_and_shuffle_file(input_file: FlyteFile) -> FlyteFile:
         """
-        Reads the input file as a DataFrame, removes a specified column, and outputs it as a new file.
+        Reads the input file as a DataFrame, shuffles the rows, and writes the shuffled DataFrame to a new file.
         """
         input_file.download()
         df = pd.read_csv(input_file.path)
 
-        # remove column
-        if column_name in df.columns:
-            df = df.drop(columns=[column_name])
+        # Shuffle the DataFrame rows
+        shuffled_df = df.sample(frac=1).reset_index(drop=True)
 
-        output_file_path = "data_finished.csv"
-        df.to_csv(output_file_path, index=False)
+        output_file_path = "data_shuffle.csv"
+        shuffled_df.to_csv(output_file_path, index=False)
 
         return FlyteFile(output_file_path)
        ...