diff --git a/tpcx_bb/queries/load_test/tpcx_bb_load_test.py b/tpcx_bb/queries/load_test/tpcx_bb_load_test.py
index 4651d6f0..c5a7acdf 100644
--- a/tpcx_bb/queries/load_test/tpcx_bb_load_test.py
+++ b/tpcx_bb/queries/load_test/tpcx_bb_load_test.py
@@ -3,9 +3,9 @@
 import os, subprocess, math, time
 
 
-config["data_dir"] = "/".join(config["data_dir"].rstrip("/").split("/")[:-1])
+config = tpcxbb_argparser()
 
-spark_schema_dir = f"{os.getcwd()}/../../spark_table_schemas/"
+spark_schema_dir = f"{os.getcwd()}/spark_table_schemas/"
 
 # these tables have extra data produced by bigbench dataGen
 refresh_tables = [
@@ -50,7 +50,7 @@ def read_csv_table(table, chunksize="256 MiB"):
     names, types = get_schema(table)
     dtype = {names[i]: types[i] for i in range(0, len(names))}
 
-    data_dir = config["data_dir"]
+    data_dir = config["data_dir"].split('parquet_')[0]
     base_path = f"{data_dir}/data/{table}"
     files = os.listdir(base_path)
     # item_marketprices has "audit" files that should be excluded
@@ -100,7 +100,7 @@ def multiplier(unit):
 
 # we use size of the CSV data on disk to determine number of Parquet partitions
 def get_size_gb(table):
-    data_dir = config["data_dir"]
+    data_dir = config["data_dir"].split('parquet_')[0]
     path = data_dir + "/data/" + table
     size = subprocess.check_output(["du", "-sh", path]).split()[0].decode("utf-8")
     unit = size[-1]
@@ -125,28 +125,14 @@ def repartition(table, outdir, npartitions=None, chunksize=None, compression="sn
     print(
         f"Converting {table} of {size} GB to {npartitions} parquet files, chunksize: {chunksize}"
     )
-    # web_clickstreams is particularly memory intensive
-    # we sacrifice a bit of speed for stability, converting half at a time
-    if table in ["web_clickstreams"]:
-        df = read_csv_table(table, chunksize)
-        half = int(df.npartitions / 2)
-        df.partitions[0:half].repartition(npartitions=int(npartitions / 2)).to_parquet(
-            outdir + table, compression=compression
-        )
-        print("Completed first half of web_clickstreams..")
-        df.partitions[half:].repartition(npartitions=int(npartitions / 2)).to_parquet(
-            outdir + table, compression=compression
-        )
-
-    else:
-        read_csv_table(table, chunksize).repartition(
-            npartitions=npartitions
-        ).to_parquet(outdir + table, compression=compression)
+    read_csv_table(table, chunksize).repartition(
+        npartitions=npartitions
+    ).to_parquet(outdir + table, compression=compression)
 
 
 def main(client, config):
     # location you want to write Parquet versions of the table data
-    data_dir = "/".join(config["data_dir"].split("/")[:-1])
+    data_dir = config["data_dir"].split('parquet_')[0]
     outdir = f"{data_dir}/parquet_{part_size}gb/"
 
     total = 0