diff --git a/docs/examples/chess_production/chess.py b/docs/examples/chess_production/chess.py
index d49c39fe38..f11fd0ff06 100644
--- a/docs/examples/chess_production/chess.py
+++ b/docs/examples/chess_production/chess.py
@@ -76,13 +76,14 @@ def load_data_with_retry(pipeline, data):
                 )
                 load_info = pipeline.run(data)
                 logger.info(str(load_info))
-                # raise on failed jobs
-                load_info.raise_on_failed_jobs()
-                # send notification
-                send_slack_message(
-                    pipeline.runtime_config.slack_incoming_hook,
-                    "Data was successfully loaded!"
-                )
+
+            # raise on failed jobs
+            load_info.raise_on_failed_jobs()
+            # send notification
+            send_slack_message(
+                pipeline.runtime_config.slack_incoming_hook,
+                "Data was successfully loaded!"
+            )
     except Exception:
         # we get here after all the failed retries
         # send notification
@@ -91,64 +92,64 @@ def load_data_with_retry(pipeline, data):
             "Something went wrong!"
         )
         raise
-    finally:
-        # we get here after a successful attempt
-        # see when load was started
-        logger.info(f"Pipeline was started: {load_info.started_at}")
-        # print the information on the first load package and all jobs inside
-        logger.info(f"First load package info: {load_info.load_packages[0]}")
-        # print the information on the first completed job in first load package
-        logger.info(
-            f"First completed job info: {load_info.load_packages[0].jobs['completed_jobs'][0]}"
-        )
 
-        # check for schema updates:
-        schema_updates = [p.schema_update for p in load_info.load_packages]
-        # send notifications if there are schema updates
-        if schema_updates:
-            # send notification
-            send_slack_message(
-                pipeline.runtime_config.slack_incoming_hook, "Schema was updated!"
-            )
+    # we get here after a successful attempt
+    # see when load was started
+    logger.info(f"Pipeline was started: {load_info.started_at}")
+    # print the information on the first load package and all jobs inside
+    logger.info(f"First load package info: {load_info.load_packages[0]}")
+    # print the information on the first completed job in first load package
+    logger.info(
+        f"First completed job info: {load_info.load_packages[0].jobs['completed_jobs'][0]}"
+    )
+
+    # check for schema updates:
+    schema_updates = [p.schema_update for p in load_info.load_packages]
+    # send notifications if there are schema updates
+    if schema_updates:
+        # send notification
+        send_slack_message(
+            pipeline.runtime_config.slack_incoming_hook, "Schema was updated!"
+        )
 
-        # To run simple tests with `sql_client`, such as checking table counts and
-        # warning if there is no data, you can use the `execute_query` method
-        with pipeline.sql_client() as client:
-            with client.execute_query("SELECT COUNT(*) FROM players") as cursor:
-                count = cursor.fetchone()[0]
-                if count == 0:
-                    logger.info("Warning: No data in players table")
-                else:
-                    logger.info(f"Players table contains {count} rows")
-
-        # To run simple tests with `normalize_info`, such as checking table counts and
-        # warning if there is no data, you can use the `row_counts` attribute.
-        normalize_info = pipeline.last_trace.last_normalize_info
-        count = normalize_info.row_counts.get("players", 0)
-        if count == 0:
-            logger.info("Warning: No data in players table")
-        else:
-            logger.info(f"Players table contains {count} rows")
-
-        # we reuse the pipeline instance below and load to the same dataset as data
-        logger.info("Saving the load info in the destination")
-        pipeline.run([load_info], table_name="_load_info")
-        # save trace to destination, sensitive data will be removed
-        logger.info("Saving the trace in the destination")
-        pipeline.run([pipeline.last_trace], table_name="_trace")
-
-        # print all the new tables/columns in
-        for package in load_info.load_packages:
-            for table_name, table in package.schema_update.items():
-                logger.info(f"Table {table_name}: {table.get('description')}")
-                for column_name, column in table["columns"].items():
-                    logger.info(f"\tcolumn {column_name}: {column['data_type']}")
-
-        # save the new tables and column schemas to the destination:
-        table_updates = [p.asdict()["tables"] for p in load_info.load_packages]
-        pipeline.run(table_updates, table_name="_new_tables")
-
-        return load_info
+    # To run simple tests with `sql_client`, such as checking table counts and
+    # warning if there is no data, you can use the `execute_query` method
+    with pipeline.sql_client() as client:
+        with client.execute_query("SELECT COUNT(*) FROM players") as cursor:
+            count = cursor.fetchone()[0]
+            if count == 0:
+                logger.info("Warning: No data in players table")
+            else:
+                logger.info(f"Players table contains {count} rows")
+
+    # To run simple tests with `normalize_info`, such as checking table counts and
+    # warning if there is no data, you can use the `row_counts` attribute.
+    normalize_info = pipeline.last_trace.last_normalize_info
+    count = normalize_info.row_counts.get("players", 0)
+    if count == 0:
+        logger.info("Warning: No data in players table")
+    else:
+        logger.info(f"Players table contains {count} rows")
+
+    # we reuse the pipeline instance below and load to the same dataset as data
+    logger.info("Saving the load info in the destination")
+    pipeline.run([load_info], table_name="_load_info")
+    # save trace to destination, sensitive data will be removed
+    logger.info("Saving the trace in the destination")
+    pipeline.run([pipeline.last_trace], table_name="_trace")
+
+    # print all the new tables/columns in
+    for package in load_info.load_packages:
+        for table_name, table in package.schema_update.items():
+            logger.info(f"Table {table_name}: {table.get('description')}")
+            for column_name, column in table["columns"].items():
+                logger.info(f"\tcolumn {column_name}: {column['data_type']}")
+
+    # save the new tables and column schemas to the destination:
+    table_updates = [p.asdict()["tables"] for p in load_info.load_packages]
+    pipeline.run(table_updates, table_name="_new_tables")
+
+    return load_info
 
 
 if __name__ == "__main__":
diff --git a/docs/website/docs/examples/chess_production/code/chess-snippets.py b/docs/website/docs/examples/chess_production/code/chess-snippets.py
index 96e5f61998..2ecf04bac7 100644
--- a/docs/website/docs/examples/chess_production/code/chess-snippets.py
+++ b/docs/website/docs/examples/chess_production/code/chess-snippets.py
@@ -87,13 +87,14 @@ def load_data_with_retry(pipeline, data):
                     )
                     load_info = pipeline.run(data)
                     logger.info(str(load_info))
-                    # raise on failed jobs
-                    load_info.raise_on_failed_jobs()
-                    # send notification
-                    send_slack_message(
-                        pipeline.runtime_config.slack_incoming_hook,
-                        "Data was successfully loaded!"
-                    )
+
+                # raise on failed jobs
+                load_info.raise_on_failed_jobs()
+                # send notification
+                send_slack_message(
+                    pipeline.runtime_config.slack_incoming_hook,
+                    "Data was successfully loaded!"
+                )
         except Exception:
             # we get here after all the failed retries
             # send notification
@@ -102,69 +103,69 @@ def load_data_with_retry(pipeline, data):
                 "Something went wrong!"
             )
             raise
-        finally:
-            # we get here after a successful attempt
-            # see when load was started
-            logger.info(f"Pipeline was started: {load_info.started_at}")
-            # print the information on the first load package and all jobs inside
-            logger.info(f"First load package info: {load_info.load_packages[0]}")
-            # print the information on the first completed job in first load package
-            logger.info(
-                f"First completed job info: {load_info.load_packages[0].jobs['completed_jobs'][0]}"
-            )
 
-            # check for schema updates:
-            schema_updates = [p.schema_update for p in load_info.load_packages]
-            # send notifications if there are schema updates
-            if schema_updates:
-                # send notification
-                send_slack_message(
-                    pipeline.runtime_config.slack_incoming_hook, "Schema was updated!"
-                )
+        # we get here after a successful attempt
+        # see when load was started
+        logger.info(f"Pipeline was started: {load_info.started_at}")
+        # print the information on the first load package and all jobs inside
+        logger.info(f"First load package info: {load_info.load_packages[0]}")
+        # print the information on the first completed job in first load package
+        logger.info(
+            f"First completed job info: {load_info.load_packages[0].jobs['completed_jobs'][0]}"
+        )
+
+        # check for schema updates:
+        schema_updates = [p.schema_update for p in load_info.load_packages]
+        # send notifications if there are schema updates
+        if schema_updates:
+            # send notification
+            send_slack_message(
+                pipeline.runtime_config.slack_incoming_hook, "Schema was updated!"
+            )
 
-            # To run simple tests with `sql_client`, such as checking table counts and
-            # warning if there is no data, you can use the `execute_query` method
-            with pipeline.sql_client() as client:
-                with client.execute_query("SELECT COUNT(*) FROM players") as cursor:
-                    count = cursor.fetchone()[0]
-                    if count == 0:
-                        logger.info("Warning: No data in players table")
-                    else:
-                        logger.info(f"Players table contains {count} rows")
-            assert count == MAX_PLAYERS  # @@@DLT_REMOVE
-
-            # To run simple tests with `normalize_info`, such as checking table counts and
-            # warning if there is no data, you can use the `row_counts` attribute.
-            normalize_info = pipeline.last_trace.last_normalize_info
-            count = normalize_info.row_counts.get("players", 0)
-            if count == 0:
-                logger.info("Warning: No data in players table")
-            else:
-                logger.info(f"Players table contains {count} rows")
-            assert count == MAX_PLAYERS  # @@@DLT_REMOVE
-
-            # we reuse the pipeline instance below and load to the same dataset as data
-            logger.info("Saving the load info in the destination")
-            pipeline.run([load_info], table_name="_load_info")
-            assert "_load_info" in pipeline.last_trace.last_normalize_info.row_counts  # @@@DLT_REMOVE
-            # save trace to destination, sensitive data will be removed
-            logger.info("Saving the trace in the destination")
-            pipeline.run([pipeline.last_trace], table_name="_trace")
-            assert "_trace" in pipeline.last_trace.last_normalize_info.row_counts  # @@@DLT_REMOVE
-
-            # print all the new tables/columns in
-            for package in load_info.load_packages:
-                for table_name, table in package.schema_update.items():
-                    logger.info(f"Table {table_name}: {table.get('description')}")
-                    for column_name, column in table["columns"].items():
-                        logger.info(f"\tcolumn {column_name}: {column['data_type']}")
-
-            # save the new tables and column schemas to the destination:
-            table_updates = [p.asdict()["tables"] for p in load_info.load_packages]
-            pipeline.run(table_updates, table_name="_new_tables")
-            assert "_new_tables" in pipeline.last_trace.last_normalize_info.row_counts  # @@@DLT_REMOVE
-
-            return load_info
+        # To run simple tests with `sql_client`, such as checking table counts and
+        # warning if there is no data, you can use the `execute_query` method
+        with pipeline.sql_client() as client:
+            with client.execute_query("SELECT COUNT(*) FROM players") as cursor:
+                count = cursor.fetchone()[0]
+                if count == 0:
+                    logger.info("Warning: No data in players table")
+                else:
+                    logger.info(f"Players table contains {count} rows")
+        assert count == MAX_PLAYERS  # @@@DLT_REMOVE
+
+        # To run simple tests with `normalize_info`, such as checking table counts and
+        # warning if there is no data, you can use the `row_counts` attribute.
+        normalize_info = pipeline.last_trace.last_normalize_info
+        count = normalize_info.row_counts.get("players", 0)
+        if count == 0:
+            logger.info("Warning: No data in players table")
+        else:
+            logger.info(f"Players table contains {count} rows")
+        assert count == MAX_PLAYERS  # @@@DLT_REMOVE
+
+        # we reuse the pipeline instance below and load to the same dataset as data
+        logger.info("Saving the load info in the destination")
+        pipeline.run([load_info], table_name="_load_info")
+        assert "_load_info" in pipeline.last_trace.last_normalize_info.row_counts  # @@@DLT_REMOVE
+        # save trace to destination, sensitive data will be removed
+        logger.info("Saving the trace in the destination")
+        pipeline.run([pipeline.last_trace], table_name="_trace")
+        assert "_trace" in pipeline.last_trace.last_normalize_info.row_counts  # @@@DLT_REMOVE
+
+        # print all the new tables/columns in
+        for package in load_info.load_packages:
+            for table_name, table in package.schema_update.items():
+                logger.info(f"Table {table_name}: {table.get('description')}")
+                for column_name, column in table["columns"].items():
+                    logger.info(f"\tcolumn {column_name}: {column['data_type']}")
+
+        # save the new tables and column schemas to the destination:
+        table_updates = [p.asdict()["tables"] for p in load_info.load_packages]
+        pipeline.run(table_updates, table_name="_new_tables")
+        assert "_new_tables" in pipeline.last_trace.last_normalize_info.row_counts  # @@@DLT_REMOVE
+
+        return load_info
 
     # @@@DLT_SNIPPET_END markdown_retry_cm
 
diff --git a/docs/website/docs/examples/chess_production/index.md b/docs/website/docs/examples/chess_production/index.md
index cb10ed189e..81524f7ee2 100644
--- a/docs/website/docs/examples/chess_production/index.md
+++ b/docs/website/docs/examples/chess_production/index.md
@@ -111,13 +111,14 @@ def load_data_with_retry(pipeline, data):
                 )
                 load_info = pipeline.run(data)
                 logger.info(str(load_info))
-                # raise on failed jobs
-                load_info.raise_on_failed_jobs()
-                # send notification
-                send_slack_message(
-                    pipeline.runtime_config.slack_incoming_hook,
-                    "Data was successfully loaded!"
-                )
+
+            # raise on failed jobs
+            load_info.raise_on_failed_jobs()
+            # send notification
+            send_slack_message(
+                pipeline.runtime_config.slack_incoming_hook,
+                "Data was successfully loaded!"
+            )
     except Exception:
         # we get here after all the failed retries
         # send notification
@@ -126,64 +127,64 @@ def load_data_with_retry(pipeline, data):
             "Something went wrong!"
         )
         raise
-    finally:
-        # we get here after a successful attempt
-        # see when load was started
-        logger.info(f"Pipeline was started: {load_info.started_at}")
-        # print the information on the first load package and all jobs inside
-        logger.info(f"First load package info: {load_info.load_packages[0]}")
-        # print the information on the first completed job in first load package
-        logger.info(
-            f"First completed job info: {load_info.load_packages[0].jobs['completed_jobs'][0]}"
-        )
 
-        # check for schema updates:
-        schema_updates = [p.schema_update for p in load_info.load_packages]
-        # send notifications if there are schema updates
-        if schema_updates:
-            # send notification
-            send_slack_message(
-                pipeline.runtime_config.slack_incoming_hook, "Schema was updated!"
-            )
+    # we get here after a successful attempt
+    # see when load was started
+    logger.info(f"Pipeline was started: {load_info.started_at}")
+    # print the information on the first load package and all jobs inside
+    logger.info(f"First load package info: {load_info.load_packages[0]}")
+    # print the information on the first completed job in first load package
+    logger.info(
+        f"First completed job info: {load_info.load_packages[0].jobs['completed_jobs'][0]}"
+    )
+
+    # check for schema updates:
+    schema_updates = [p.schema_update for p in load_info.load_packages]
+    # send notifications if there are schema updates
+    if schema_updates:
+        # send notification
+        send_slack_message(
+            pipeline.runtime_config.slack_incoming_hook, "Schema was updated!"
+        )
 
-        # To run simple tests with `sql_client`, such as checking table counts and
-        # warning if there is no data, you can use the `execute_query` method
-        with pipeline.sql_client() as client:
-            with client.execute_query("SELECT COUNT(*) FROM players") as cursor:
-                count = cursor.fetchone()[0]
-                if count == 0:
-                    logger.info("Warning: No data in players table")
-                else:
-                    logger.info(f"Players table contains {count} rows")
-
-        # To run simple tests with `normalize_info`, such as checking table counts and
-        # warning if there is no data, you can use the `row_counts` attribute.
-        normalize_info = pipeline.last_trace.last_normalize_info
-        count = normalize_info.row_counts.get("players", 0)
-        if count == 0:
-            logger.info("Warning: No data in players table")
-        else:
-            logger.info(f"Players table contains {count} rows")
-
-        # we reuse the pipeline instance below and load to the same dataset as data
-        logger.info("Saving the load info in the destination")
-        pipeline.run([load_info], table_name="_load_info")
-        # save trace to destination, sensitive data will be removed
-        logger.info("Saving the trace in the destination")
-        pipeline.run([pipeline.last_trace], table_name="_trace")
-
-        # print all the new tables/columns in
-        for package in load_info.load_packages:
-            for table_name, table in package.schema_update.items():
-                logger.info(f"Table {table_name}: {table.get('description')}")
-                for column_name, column in table["columns"].items():
-                    logger.info(f"\tcolumn {column_name}: {column['data_type']}")
-
-        # save the new tables and column schemas to the destination:
-        table_updates = [p.asdict()["tables"] for p in load_info.load_packages]
-        pipeline.run(table_updates, table_name="_new_tables")
-
-        return load_info
+    # To run simple tests with `sql_client`, such as checking table counts and
+    # warning if there is no data, you can use the `execute_query` method
+    with pipeline.sql_client() as client:
+        with client.execute_query("SELECT COUNT(*) FROM players") as cursor:
+            count = cursor.fetchone()[0]
+            if count == 0:
+                logger.info("Warning: No data in players table")
+            else:
+                logger.info(f"Players table contains {count} rows")
+
+    # To run simple tests with `normalize_info`, such as checking table counts and
+    # warning if there is no data, you can use the `row_counts` attribute.
+    normalize_info = pipeline.last_trace.last_normalize_info
+    count = normalize_info.row_counts.get("players", 0)
+    if count == 0:
+        logger.info("Warning: No data in players table")
+    else:
+        logger.info(f"Players table contains {count} rows")
+
+    # we reuse the pipeline instance below and load to the same dataset as data
+    logger.info("Saving the load info in the destination")
+    pipeline.run([load_info], table_name="_load_info")
+    # save trace to destination, sensitive data will be removed
+    logger.info("Saving the trace in the destination")
+    pipeline.run([pipeline.last_trace], table_name="_trace")
+
+    # print all the new tables/columns in
+    for package in load_info.load_packages:
+        for table_name, table in package.schema_update.items():
+            logger.info(f"Table {table_name}: {table.get('description')}")
+            for column_name, column in table["columns"].items():
+                logger.info(f"\tcolumn {column_name}: {column['data_type']}")
+
+    # save the new tables and column schemas to the destination:
+    table_updates = [p.asdict()["tables"] for p in load_info.load_packages]
+    pipeline.run(table_updates, table_name="_new_tables")
+
+    return load_info
 ```
 <!--@@@DLT_SNIPPET_END ./code/chess-snippets.py::markdown_retry_cm-->