diff --git a/gpu_bdb/bdb_tools/q25_utils.py b/gpu_bdb/bdb_tools/q25_utils.py
index 523598f5..cffdcccf 100644
--- a/gpu_bdb/bdb_tools/q25_utils.py
+++ b/gpu_bdb/bdb_tools/q25_utils.py
@@ -14,6 +14,7 @@
 # limitations under the License.
 #
 
+import dask.dataframe as dd
 import dask_cudf
 
 from bdb_tools.utils import train_clustering_model
@@ -33,6 +34,7 @@ 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"]
@@ -65,10 +67,15 @@ 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)
-
-    labels_final = dask_cudf.from_cudf(
-        results_dict["cid_labels"], npartitions=output.npartitions
-    )
+    
+    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
+        )
     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 5f299565..01f421c6 100644
--- a/gpu_bdb/bdb_tools/q26_utils.py
+++ b/gpu_bdb/bdb_tools/q26_utils.py
@@ -26,6 +26,7 @@ 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 2d8e3309..e85f5dad 100644
--- a/gpu_bdb/bdb_tools/q30_utils.py
+++ b/gpu_bdb/bdb_tools/q30_utils.py
@@ -28,6 +28,7 @@ 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 1b3cfd1d..2383d9fc 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
@@ -34,8 +34,11 @@
 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()
@@ -43,10 +46,16 @@ 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)
-
-    labels_final = dask_cudf.from_cudf(
-        results_dict["cid_labels"], npartitions=output.npartitions
-    )
+    
+    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
+        )
+        
     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 a473bdc5..7a3e8fbf 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
@@ -35,8 +35,11 @@
 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()
@@ -45,10 +48,16 @@ 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)
-
-    labels_final = dask_cudf.from_cudf(
-        results_dict["cid_labels"], npartitions=output.npartitions
-    )
+    
+    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
+        )
+        
     output["label"] = labels_final.reset_index()[0]
 
     # Based on CDH6.1 q26-result formatting