diff --git a/abalon/spark/pivoter.py b/abalon/spark/pivoter.py
index 1f9e41f..e6ceac1 100644
--- a/abalon/spark/pivoter.py
+++ b/abalon/spark/pivoter.py
@@ -90,11 +90,12 @@ def AggSparkPivoter (df, all_vars=None, agg_op=operator.add):
     '''
 
     def agg_merge_two_dicts(x, y, agg_op):
-        return {k: agg_op(float(x.get(k, 0)),
+        z = {k: agg_op(float(x.get(k, 0)),
                           float(y.get(k, 0))
                          )
-                for k in set(x).union(y)
+                    for k in set(x).union(y)
                 }
+        return z
 
     return pivot_df(df, agg_merge_two_dicts, all_vars, agg_op)
 
@@ -120,6 +121,9 @@ def map_dict_to_denseArray(idx, d):
         # get list of variables from the dataset:
         all_vars = sorted([row[0] for row in df.select(key_col).distinct().collect()])
 
+    # key becomes column names, so make it sql-complaint
+    columns = [field_name.strip().replace('.','_') for field_name in all_vars]
+
     pivoted_rdd = (df.rdd
         .map(lambda (idx, k, v): (idx, {k: v}))  # convert k,v to a 1-element dict
         .reduceByKey(lambda x,y: merger_func(x, y, agg_op))  # merge into a single dict for all vars for this idx
@@ -127,8 +131,8 @@ def map_dict_to_denseArray(idx, d):
                                         # create final rdd with dense array of all variables
     )
 
-    fields =  [StructField(idx_col,    StringType(), False)]
-    fields += [StructField(field_name, DoubleType(), True) for field_name in all_vars]
+    fields =  [StructField(idx_col, StringType(), False)]
+    fields += [StructField(column,  DoubleType(), True) for column in columns]
 
     schema = StructType(fields)
 
diff --git a/abalon/version.py b/abalon/version.py
index 527aad2..af51104 100644
--- a/abalon/version.py
+++ b/abalon/version.py
@@ -12,6 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-version = '2.2.4'
+version = '2.2.5'