diff --git a/gpu_bdb/queries/load_test/gpu_bdb_load_test.py b/gpu_bdb/queries/load_test/gpu_bdb_load_test.py
index ab57193f..1e9dff6f 100755
--- a/gpu_bdb/queries/load_test/gpu_bdb_load_test.py
+++ b/gpu_bdb/queries/load_test/gpu_bdb_load_test.py
@@ -1,5 +1,6 @@
 from bdb_tools.utils import benchmark, gpubdb_argparser, run_query
 from bdb_tools.readers import build_reader
+from cudf.core.dtypes import Decimal64Dtype
 import os, subprocess, math, time
 
 
@@ -33,22 +34,21 @@ def get_schema(table):
         schema = fp.read()
         names = [line.replace(",", "").split()[0] for line in schema.split("\n")]
         types = [
-            line.replace(",", "")
+            line.replace(", ", "")
             .split()[1]
             .replace("bigint", "int")
             .replace("string", "str")
             for line in schema.split("\n")
         ]
-        types = [
-            col_type.split("(")[0].replace("decimal", "float") for col_type in types
-        ]
+
         return names, types
 
 
 def read_csv_table(table, chunksize="256 MiB"):
     # build dict of dtypes to use when reading CSV
     names, types = get_schema(table)
-    dtype = {names[i]: types[i] for i in range(0, len(names))}
+    types_dec_as_str = ['str' if 'decimal' in t else t for t in types]
+    dtype_dec_as_str = {names[i]: types_dec_as_str[i] for i in range(0, len(names))}
 
     data_dir = config["data_dir"].split('parquet_')[0]
     base_path = f"{data_dir}/data/{table}"
@@ -67,7 +67,7 @@ def read_csv_table(table, chunksize="256 MiB"):
             if "audit" not in fn and os.path.getsize(f"{base_path}/{fn}") > 0
         ]
         df = dask_cudf.read_csv(
-            paths, sep="|", names=names, dtype=dtype, chunksize=chunksize, quoting=3
+            paths, sep="|", names=names, dtype=dtype_dec_as_str, chunksize=chunksize, quoting=3
         )
     else:
         paths = [
@@ -83,9 +83,15 @@ def read_csv_table(table, chunksize="256 MiB"):
                 if os.path.getsize(f"{base_path}/{fn}") > 0
             ]
         df = dask_cudf.read_csv(
-            paths, sep="|", names=names, dtype=types, chunksize=chunksize, quoting=3
+            paths, sep="|", names=names, dtype=types_dec_as_str, chunksize=chunksize, quoting=3
         )
 
+    for i, dtype in enumerate(types):
+        if 'decimal' in dtype:
+            precision = int(dtype[dtype.find("(")+1:dtype.find(",")])
+            scale = int(dtype[dtype.find(",")+1:dtype.find(")")])
+            df[names[i]] = df[names[i]].astype(Decimal64Dtype(precision, scale))
+
     return df
 
 
@@ -133,7 +139,7 @@ def repartition(table, outdir, npartitions=None, chunksize=None, compression="sn
 def main(client, config):
     # location you want to write Parquet versions of the table data
     data_dir = config["data_dir"].split('parquet_')[0]
-    outdir = f"{data_dir}/parquet_{part_size}gb/"
+    outdir = f"{data_dir}/parquet_{part_size}gb_decimal/"
 
     t0 = time.time()
     for table in tables: