diff --git a/examples/ransomware_detection/run.py b/examples/ransomware_detection/run.py
index 4887e7ff1b..410cb4386a 100644
--- a/examples/ransomware_detection/run.py
+++ b/examples/ransomware_detection/run.py
@@ -21,9 +21,12 @@
 from stages.create_features import CreateFeaturesRWStage
 from stages.preprocessing import PreprocessingRWStage
 
+from morpheus.common import TypeId
 from morpheus.config import Config
 from morpheus.config import PipelineModes
+from morpheus.messages import MessageMeta
 from morpheus.pipeline.linear_pipeline import LinearPipeline
+from morpheus.pipeline.stage_decorator import stage
 from morpheus.stages.general.monitor_stage import MonitorStage
 from morpheus.stages.inference.triton_inference_stage import TritonInferenceStage
 from morpheus.stages.input.appshield_source_stage import AppShieldSourceStage
@@ -61,6 +64,12 @@
     type=click.IntRange(min=1),
     help="Max batch size to use for the model.",
 )
+@click.option(
+    "--pipeline_batch_size",
+    default=1024,
+    type=click.IntRange(min=1),
+    help=("Internal batch size for the pipeline. Can be much larger than the model batch size."),
+)
 @click.option(
     "--conf_file",
     type=click.STRING,
@@ -98,18 +107,19 @@
     default="./ransomware_detection_output.jsonlines",
     help="The path to the file where the inference output will be saved.",
 )
-def run_pipeline(debug,
-                 num_threads,
-                 n_dask_workers,
-                 threads_per_dask_worker,
-                 model_max_batch_size,
-                 conf_file,
-                 model_name,
-                 server_url,
-                 sliding_window,
-                 input_glob,
-                 watch_directory,
-                 output_file):
+def run_pipeline(debug: bool,
+                 num_threads: int,
+                 n_dask_workers: int,
+                 threads_per_dask_worker: int,
+                 model_max_batch_size: int,
+                 pipeline_batch_size: int,
+                 conf_file: str,
+                 model_name: str,
+                 server_url: str,
+                 sliding_window: int,
+                 input_glob: str,
+                 watch_directory: bool,
+                 output_file: str):
 
     if debug:
         configure_logging(log_level=logging.DEBUG)
@@ -125,6 +135,7 @@ def run_pipeline(debug,
     # Below properties are specified by the command line.
     config.num_threads = num_threads
     config.model_max_batch_size = model_max_batch_size
+    config.pipeline_batch_size = pipeline_batch_size
     config.feature_length = snapshot_fea_length * sliding_window
     config.class_labels = ["pred", "score"]
 
@@ -222,6 +233,18 @@ def run_pipeline(debug,
     # This stage logs the metrics (msg/sec) from the above stage.
     pipeline.add_stage(MonitorStage(config, description="Serialize rate"))
 
+    @stage(needed_columns={'timestamp_process': TypeId.STRING})
+    def concat_columns(msg: MessageMeta) -> MessageMeta:
+        """
+        This stage concatinates the timestamp and pid_process columns to create a unique field.
+        """
+        with msg.mutable_dataframe() as df:
+            df['timestamp_process'] = df['timestamp'] + df['pid_process']
+
+        return msg
+
+    pipeline.add_stage(concat_columns(config))
+
     # Add a write file stage.
     # This stage writes all messages to a file.
     pipeline.add_stage(WriteToFileStage(config, filename=output_file, overwrite=True))