diff --git a/examples/__init__.py b/examples/__init__.py
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diff --git a/examples/processing_large_csv/__init__.py b/examples/processing_large_csv/__init__.py
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index 0000000..e69de29
diff --git a/examples/processing_large_csv/cio_event_manager.py b/examples/processing_large_csv/cio_event_manager.py
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+++ b/examples/processing_large_csv/cio_event_manager.py
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+"""
+Sample code to demonstrate sending data, contained in CSVs, to CustomerIO as events. This script uses Python3.
+"""
+import csv
+import time
+import logging
+import os
+from multiprocessing.pool import ThreadPool as Pool
+from customerio import CustomerIO, CustomerIOException
+
+logger = logging.getLogger(__name__)
+
+
+class CIOEventManager(object):
+    def __init__(self, cio_site_id, cio_api_key, csv_full_file_path):
+        self.site_id = cio_site_id
+        self.api_key = cio_api_key
+        self.csv_full_file_path = os.path.normpath(csv_full_file_path)
+        self.cio_api_client = CustomerIO(self.site_id, self.api_key, retries=5)
+        self.event_name = None
+
+    def _read_csv(self):
+        with open(self.csv_full_file_path, 'r') as csv_file:
+            data = csv.DictReader(csv_file)
+            for line in data:
+                yield line
+
+    def _send_data_to_cio(self, data):
+        try:
+            # Assume each CSV row looks like
+            # {'age': '23', 'created_at': '', 'location': 'Boston', 'id': 'id: 1', 'unsubscribed': 'false'}
+            self.cio_api_client.track(customer_id=data['id'], name=self.event_name)
+        except CustomerIOException as e:
+            # log the CSV row and exception and move to processing the next row
+            logger.exception("Failed to send the row data: {row}. Error message={message}".format(row=data,
+                                                                                                  message=e.message))
+
+    def pre_process(self):
+        """Use this method to do some tasks before the CSV is processed"""
+        pass
+
+    def send_events(self, no_of_processes=4):
+        """
+        First runs some pre-processing. Read the CSV and send data from each row to CustomerIO. Then it finally
+        runs some post-processing.
+        """
+        self.pre_process()
+        csv = self._read_csv()
+        pool = Pool(processes=no_of_processes)
+        for row in csv:
+            pool.map(self._send_data_to_cio, (row,))
+        rate_limit_in_secs = 0.03  # rate limiting 30 requests per sec (i.e. 1  / 30 = 0.03)
+        time.sleep(rate_limit_in_secs)
+        self._post_process()
+        pool.close()
+        pool.join()
+
+    def _post_process(self):
+        """Use this method to do some tasks after the CSV is processed"""
+        pass
diff --git a/examples/processing_large_csv/readme.MD b/examples/processing_large_csv/readme.MD
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+++ b/examples/processing_large_csv/readme.MD
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+# Sending event data to CustomerIO from CSVs 
+
+
+Let's say you have large amounts of data in CSV. And you need to process
+each row, and then send data from that row to CustomerIO. 
+
+Well, there are many ways one to do this. But we have demonstrated one way to do that
+in the file `cio_event_manager.py`.
+
+To run the code, you will need the following data:
+* Site-ID and API Key from your CustomerIO workspace
+* Event name
+* Full CSV file path
+
+
+And then run the code like this:
+````
+cio_event_manager = CIOEventManager(site_id, api_key, full_file_path_in_str)
+cio_event_manager.event_name = 'purchase'
+cio_event_manager.send_events()
+````
+
+Behind the scenes, the CSV will be read, and data will be sent to CustomerIO. To speed
+up the process of sending data, parallelism is demonstrated using Python's `Multiprocessing` module. 
+