diff --git a/distributed_produce/README.md b/distributed_produce/README.md
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index 638e5a0..0000000
--- a/distributed_produce/README.md
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-```
-   ___   _      __        _  __         __           __
-  / _ \ (_)___ / /_ ____ (_)/ /  __ __ / /_ ___  ___/ /
- / // // /(_-</ __// __// // _ \/ // // __// -_)/ _  / 
-/____//_//___/\__//_/ _/_//_.__/\_,_/ \__/ \__/ \_,_/  
-  / _ \ ____ ___  ___/ /__ __ ____ ___                 
- / ___// __// _ \/ _  // // // __// -_)                
-/_/   /_/   \___/\_,_/ \_,_/ \__/ \__/                 
-by Joshua E. Jodesty                                                       
-                                       
-```
-
-**Description:**
-Distributed Produce (**distroduce**) is a cadCAD feature leveraging Apache Spark and Apache Kafka to enable in-stream 
-data processing application development and throughput benchmarking for Kafka clusters. 
-
-**Properties:**
-* enables cadCAD's user-defined simulation framework to publish events/messages to Kafka Clusters
-* enables scalable message publishing Kafka clusters by distributing simulated event/message creation and publishing on
-an EMR cluster using Spark and Kafka Producer
-
-
-#### Installation / Build From Source:
-```
-pip3 install -r requirements.txt
-zip -rq distributed_produce/dist/distroduce.zip cadCAD/distroduce/
-```
-
-#### Usage:
-```
-spark-submit --py-files distributed_produce/dist/distroduce.zip distributed_produce/examples/event_bench/main.py
-```
diff --git a/distributed_produce/ascii_art.txt b/distributed_produce/ascii_art.txt
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--- a/distributed_produce/ascii_art.txt
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@@ -1,10 +0,0 @@
-   ___   _      __        _  __         __           __
-  / _ \ (_)___ / /_ ____ (_)/ /  __ __ / /_ ___  ___/ /
- / // // /(_-</ __// __// // _ \/ // // __// -_)/ _  /
-/____//_//___/\__//_/ _/_//_.__/\_,_/ \__/ \__/ \_,_/
-  / _ \ ____ ___  ___/ /__ __ ____ ___
- / ___// __// _ \/ _  // // // __// -_)
-/_/   /_/   \___/\_,_/ \_,_/ \__/ \__/
-by Joshua E. Jodesty
-
-promulgate
diff --git a/distributed_produce/dist/distroduce.zip b/distributed_produce/dist/distroduce.zip
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index 6ad95b8..0000000
Binary files a/distributed_produce/dist/distroduce.zip and /dev/null differ
diff --git a/distributed_produce/examples/event_bench/consumer.py b/distributed_produce/examples/event_bench/consumer.py
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--- a/distributed_produce/examples/event_bench/consumer.py
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-from kafka import KafkaConsumer
-from datetime import datetime
-
-consumer = KafkaConsumer('test', bootstrap_servers=['localhost:9092'])
-
-i = 1
-for message in consumer:
-    percent = i / 10000
-    print(str(datetime.now()) + " - " + str(percent) + ": " + str(i))
-    i += 1
diff --git a/distributed_produce/examples/event_bench/main.py b/distributed_produce/examples/event_bench/main.py
deleted file mode 100644
index e4a96a2..0000000
--- a/distributed_produce/examples/event_bench/main.py
+++ /dev/null
@@ -1,58 +0,0 @@
-from functools import reduce
-from datetime import timedelta
-from pprint import pprint
-import time
-from pathos.multiprocessing import ThreadPool as TPool
-
-from cadCAD.utils import flatten
-from cadCAD.distroduce.executor import distributed_produce
-from distributed_produce.examples.event_bench.spark.session import spark_context as sc
-
-
-def main(sc, spark_runs, rdd_parts, sim_time, sim_runs, parameterized_message):
-    publish_times, spark_job_times = [], []
-    prod_config = {'bootstrap_servers': 'localhost:9092', 'acks': 'all'}
-    exec_spark_job = lambda run: distributed_produce(
-        sc, run, sim_time, sim_runs, rdd_parts, parameterized_message, prod_config
-    )
-
-    job = 0
-    with TPool(spark_runs) as t:
-        start_time = time.time()
-        publish_times.append(t.map(exec_spark_job, range(spark_runs)))
-        spark_job_times.append({'job_num': job, 'job_time': time.time() - start_time})
-        job += 1
-
-    publish_times = sorted(flatten(list(reduce(lambda a, b: a + b, publish_times))), key=lambda x: x[0])
-    # publish_start_sec = list(set([time[0].second for time in publish_times]))
-    publish_send_secs = [time[1] for time in publish_times]
-    publish_send_dts = [time[0] for time in publish_times]
-
-    send_time = publish_send_dts[-1] - publish_send_dts[0] + timedelta(microseconds=1000000 * publish_send_secs[-1])
-    sent_messages = sim_time * sim_runs
-    print(f"Spark Job Times: ")
-    pprint(spark_job_times)
-    print()
-    print(f"Messages per Second: {float(sent_messages) / (float(send_time.seconds) + float(send_time.microseconds/1000000))}")
-    print(f"Sent Messages: {sent_messages}")
-    print(f"Send Time (Seconds): {send_time}")
-    print(f"Avg. Send Loop Duration: {sum(publish_send_secs)/(spark_runs*25)}")
-
-
-if __name__ == "__main__":
-    sc.setLogLevel("ERROR")
-    spark_runs = 1
-    sim_time = 400
-    sim_runs = 25
-    rdd_parts = 8
-
-    def msg(t):
-        return str(f"*****************message_{t}").encode('utf-8')
-
-    main(sc, spark_runs, rdd_parts, sim_time, sim_runs, msg)
-
-    # prod_config = {'bootstrap_servers': 'localhost:9092', 'acks': 0, 'max_in_flight_requests_per_connection': 1, 'batch_size': 0, 'retries': 0}
-    # print(f"Second Starts: {len(publish_start_sec)}")
-    # print(f"Start Seconds: {publish_start_sec}")
-    # pprint(publish_send_secs)
-    # pprint(publish_times)
diff --git a/distributed_produce/examples/event_bench/spark/session/__init__.py b/distributed_produce/examples/event_bench/spark/session/__init__.py
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-from pyspark.sql import SparkSession
-from pyspark.context import SparkContext
-# import os
-
-# os.environ['PYSPARK_PYTHON'] = '/usr/local/bin/python3'
-# os.environ['PYSPARK_DRIVER_PYTHON'] = '/usr/local/bin/python3'
-# os.environ['OBJC_DISABLE_INITIALIZE_FORK_SAFETY'] = 'YES'
-
-spark = SparkSession\
-        .builder\
-        .appName("DistributedProduce")\
-        .getOrCreate()
-
-spark_context: SparkContext = spark.sparkContext
-print(f"Spark UI: {spark_context.uiWebUrl}")
-print()
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