diff --git a/gpu_bdb/bdb_tools/q25_utils.py b/gpu_bdb/bdb_tools/q25_utils.py
index cffdcccf..523598f5 100644
--- a/gpu_bdb/bdb_tools/q25_utils.py
+++ b/gpu_bdb/bdb_tools/q25_utils.py
@@ -14,7 +14,6 @@
 # limitations under the License.
 #
 
-import dask.dataframe as dd
 import dask_cudf
 
 from bdb_tools.utils import train_clustering_model
@@ -34,7 +33,6 @@ def read_tables(config, c=None):
         data_format=config["file_format"],
         basepath=config["data_dir"],
         split_row_groups=config["split_row_groups"],
-        backend=config["backend"],
     )
 
     ss_cols = ["ss_customer_sk", "ss_sold_date_sk", "ss_ticket_number", "ss_net_paid"]
@@ -67,15 +65,10 @@ def get_clusters(client, ml_input_df):
     results_dict = client.compute(*ml_tasks, sync=True)
 
     output = ml_input_df.index.to_frame().reset_index(drop=True)
-    
-    if isinstance(ml_input_df, cudf.DataFrame):
-        labels_final = dask_cudf.from_cudf(
-            results_dict["cid_labels"], npartitions=output.npartitions
-        )
-    else:
-         labels_final = dd.from_cudf(
-            results_dict["cid_labels"], npartitions=output.npartitions
-        )
+
+    labels_final = dask_cudf.from_cudf(
+        results_dict["cid_labels"], npartitions=output.npartitions
+    )
     output["label"] = labels_final.reset_index()[0]
 
     # Sort based on CDH6.1 q25-result formatting
diff --git a/gpu_bdb/bdb_tools/q26_utils.py b/gpu_bdb/bdb_tools/q26_utils.py
index 01f421c6..5f299565 100644
--- a/gpu_bdb/bdb_tools/q26_utils.py
+++ b/gpu_bdb/bdb_tools/q26_utils.py
@@ -26,7 +26,6 @@ def read_tables(config, c=None):
         data_format=config["file_format"],
         basepath=config["data_dir"],
         split_row_groups=config["split_row_groups"],
-        backend=config["backend"],
     )
 
     ss_cols = ["ss_customer_sk", "ss_item_sk"]
diff --git a/gpu_bdb/bdb_tools/q30_utils.py b/gpu_bdb/bdb_tools/q30_utils.py
index e85f5dad..2d8e3309 100644
--- a/gpu_bdb/bdb_tools/q30_utils.py
+++ b/gpu_bdb/bdb_tools/q30_utils.py
@@ -28,7 +28,6 @@ def read_tables(config, c=None):
         data_format=config["file_format"],
         basepath=config["data_dir"],
         split_row_groups=config["split_row_groups"],
-        backend=config["backend"],
     )
 
     item_cols = ["i_category_id", "i_item_sk"]
diff --git a/gpu_bdb/queries/q25/gpu_bdb_query_25_dask_sql.py b/gpu_bdb/queries/q25/gpu_bdb_query_25_dask_sql.py
index e43357ec..a9257ff1 100755
--- a/gpu_bdb/queries/q25/gpu_bdb_query_25_dask_sql.py
+++ b/gpu_bdb/queries/q25/gpu_bdb_query_25_dask_sql.py
@@ -36,11 +36,8 @@
 from dask import delayed
 
 def get_clusters(client, ml_input_df):
-    import dask.dataframe as dd
     import dask_cudf
-    import cudf
-    import pandas as pd
-    
+
     ml_tasks = [
         delayed(train_clustering_model)(df, N_CLUSTERS, CLUSTER_ITERATIONS, N_ITER)
         for df in ml_input_df.to_delayed()
@@ -48,16 +45,10 @@ def get_clusters(client, ml_input_df):
     results_dict = client.compute(*ml_tasks, sync=True)
 
     output = ml_input_df.index.to_frame().reset_index(drop=True)
-    
-    if isinstance(ml_input_df, cudf.DataFrame):
-        labels_final = dask_cudf.from_cudf(
-            results_dict["cid_labels"], npartitions=output.npartitions
-        )
-    else:
-        labels_final = dd.from_pandas(
-            pd.DataFrame(results_dict["cid_labels"]), npartitions=output.npartitions
-        )
-        
+
+    labels_final = dask_cudf.from_cudf(
+        results_dict["cid_labels"], npartitions=output.npartitions
+    )
     output["label"] = labels_final.reset_index()[0]
 
     # Based on CDH6.1 q25-result formatting
diff --git a/gpu_bdb/queries/q26/gpu_bdb_query_26_dask_sql.py b/gpu_bdb/queries/q26/gpu_bdb_query_26_dask_sql.py
index b3d68089..354fbb63 100755
--- a/gpu_bdb/queries/q26/gpu_bdb_query_26_dask_sql.py
+++ b/gpu_bdb/queries/q26/gpu_bdb_query_26_dask_sql.py
@@ -36,11 +36,8 @@
 from dask import delayed
 
 def get_clusters(client, kmeans_input_df):
-    import dask.dataframe as dd
     import dask_cudf
-    import cudf
-    import pandas as pd
-    
+
     ml_tasks = [
         delayed(train_clustering_model)(df, N_CLUSTERS, CLUSTER_ITERATIONS, N_ITER)
         for df in kmeans_input_df.to_delayed()
@@ -49,16 +46,10 @@ def get_clusters(client, kmeans_input_df):
     results_dict = client.compute(*ml_tasks, sync=True)
 
     output = kmeans_input_df.index.to_frame().reset_index(drop=True)
-    
-    if isinstance(kmeans_input_df, cudf.DataFrame):
-        labels_final = dask_cudf.from_cudf(
-            results_dict["cid_labels"], npartitions=output.npartitions
-        )
-    else:
-        labels_final = dd.from_pandas(
-            pd.DataFrame(results_dict["cid_labels"]), npartitions=output.npartitions
-        )
-        
+
+    labels_final = dask_cudf.from_cudf(
+        results_dict["cid_labels"], npartitions=output.npartitions
+    )
     output["label"] = labels_final.reset_index()[0]
 
     # Based on CDH6.1 q26-result formatting