diff --git a/spark/src/main/scala/ai/chronon/spark/GroupBy.scala b/spark/src/main/scala/ai/chronon/spark/GroupBy.scala
index 635756bf7..6dda16098 100644
--- a/spark/src/main/scala/ai/chronon/spark/GroupBy.scala
+++ b/spark/src/main/scala/ai/chronon/spark/GroupBy.scala
@@ -35,6 +35,7 @@ import org.slf4j.LoggerFactory
 import java.util
 import scala.collection.{Seq, mutable}
 import scala.util.ScalaJavaConversions.{JListOps, ListOps, MapOps}
+import scala.util.Try
 
 class GroupBy(val aggregations: Seq[api.Aggregation],
               val keyColumns: Seq[String],
@@ -515,22 +516,21 @@ object GroupBy {
     // Generate mutation Df if required, align the columns with inputDf so no additional schema is needed by aggregator.
     val mutationSources = groupByConf.sources.toScala.filter { _.isSetEntities }
     val mutationsColumnOrder = inputDf.columns ++ Constants.MutationFields.map(_.name)
+    val mutationQueriesTry = Try(
+      mutationSources.map(ms =>
+        renderDataSourceQuery(groupByConf,
+                              ms,
+                              groupByConf.getKeyColumns.toScala,
+                              queryRange.shift(1),
+                              tableUtils,
+                              groupByConf.maxWindow,
+                              groupByConf.inferredAccuracy,
+                              mutations = true)))
+
     def mutationDfFn(): DataFrame = {
       val df: DataFrame = if (groupByConf.inferredAccuracy == api.Accuracy.TEMPORAL && mutationSources.nonEmpty) {
-        val mutationDf = mutationSources
-          .map {
-            renderDataSourceQuery(groupByConf,
-                                  _,
-                                  groupByConf.getKeyColumns.toScala,
-                                  queryRange.shift(1),
-                                  tableUtils,
-                                  groupByConf.maxWindow,
-                                  groupByConf.inferredAccuracy,
-                                  mutations = true)
-          }
-          .map {
-            tableUtils.sql
-          }
+        val mutationDf = mutationQueriesTry.get
+          .map { tableUtils.sql }
           .reduce { (df1, df2) =>
             val columns1 = df1.schema.fields.map(_.name)
             df1.union(df2.selectExpr(columns1: _*))