docs(batch): explain record type discrepancy in failure and success handler

docs/utilities/batch.md

@@ -522,14 +522,19 @@ You might want to bring custom logic to the existing `BatchProcessor` to slightl
 
 For these scenarios, you can subclass `BatchProcessor` and quickly override `success_handler` and `failure_handler` methods:
 
-* **`success_handler()`** – Keeps track of successful batch records
-* **`failure_handler()`** – Keeps track of failed batch records
+* **`success_handler()`** is called for each successfully processed record
+* **`failure_handler()`** is called for each failed record
 
-???+ example
-	Let's suppose you'd like to add a metric named `BatchRecordFailures` for each batch record that failed processing
+???+ note
+    These functions have a common `record` argument. For backward compatibility reasons, their type is not the same:
 
-```python hl_lines="8 9 16-19 22 38" title="Extending failure handling mechanism in BatchProcessor"
---8<-- "examples/batch_processing/src/extending_failure.py"
+    - `success_handler`: `record` type is `dict[str, Any]`, the raw record data.
+    - `failure_handler`: `record` type can be an Event Source Data Class or your [Pydantic model](#pydantic-integration). During Pydantic validation errors, we fall back and serialize `record` to Event Source Data Class to not break the processing pipeline.
+
+Let's suppose you'd like to add metrics to track successes and failures of your batch records.
+
+```python hl_lines="8-10 18-25 28 44" title="Extending failure handling mechanism in BatchProcessor"
+--8<-- "examples/batch_processing/src/extending_processor_handlers.py"
 ```
 
 ### Create your own partial processor

examples/batch_processing/src/extending_failure.py → examples/batch_processing/src/extending_processor_handlers.py

@@ -1,4 +1,5 @@
 import json
+from typing import Any
 
 from aws_lambda_powertools import Logger, Metrics, Tracer
 from aws_lambda_powertools.metrics import MetricUnit
@@ -9,11 +10,16 @@
     FailureResponse,
     process_partial_response,
 )
+from aws_lambda_powertools.utilities.batch.base import SuccessResponse
 from aws_lambda_powertools.utilities.data_classes.sqs_event import SQSRecord
 from aws_lambda_powertools.utilities.typing import LambdaContext
 
 
 class MyProcessor(BatchProcessor):
+    def success_handler(self, record: dict[str, Any], result: Any) -> SuccessResponse:
+        metrics.add_metric(name="BatchRecordSuccesses", unit=MetricUnit.Count, value=1)
+        return super().success_handler(record, result)
+
     def failure_handler(self, record: SQSRecord, exception: ExceptionInfo) -> FailureResponse:
         metrics.add_metric(name="BatchRecordFailures", unit=MetricUnit.Count, value=1)
         return super().failure_handler(record, exception)