diff --git a/utils/python/hsfs_utils.py b/utils/python/hsfs_utils.py
index ac2f23de2..fc226dca4 100644
--- a/utils/python/hsfs_utils.py
+++ b/utils/python/hsfs_utils.py
@@ -301,22 +301,25 @@ def offline_fg_materialization(spark: SparkSession, job_conf: Dict[Any, Any], in
         .option("includeHeaders", "true")
         .option("failOnDataLoss", "false")
         .load()
-        .limit(5000000)
     )
 
     # filter only the necassary entries
-    df = df.filter(expr("CAST(filter(headers, header -> header.key = 'featureGroupId')[0].value AS STRING)") == str(entity._id))
-    df = df.filter(expr("CAST(filter(headers, header -> header.key = 'subjectId')[0].value AS STRING)") == str(entity.subject["id"]))
+    filtered_df = df.filter(expr("CAST(filter(headers, header -> header.key = 'featureGroupId')[0].value AS STRING)") == str(entity._id))
+    filtered_df = filtered_df.filter(expr("CAST(filter(headers, header -> header.key = 'subjectId')[0].value AS STRING)") == str(entity.subject["id"]))
+
+    # limit the number of records ingested
+    limit = job_conf.get("write_options", {}).get("job_limit", 5000000)
+    filtered_df = filtered_df.limit(limit)
 
     # deserialize dataframe so that it can be properly saved
-    deserialized_df = engine.get_instance()._deserialize_from_avro(entity, df)
+    deserialized_df = engine.get_instance()._deserialize_from_avro(entity, filtered_df)
 
     # insert data
     entity.stream = False # to make sure we dont write to kafka
     entity.insert(deserialized_df, storage="offline")
 
     # update offsets
-    df_offsets = df.groupBy('partition').agg(max('offset').alias('offset')).collect()
+    df_offsets = (df if limit > filtered_df.count() else filtered_df).groupBy('partition').agg(max('offset').alias('offset')).collect()
     offset_dict = json.loads(offset_string)
     for offset_row in df_offsets:
         offset_dict[f"{entity._online_topic_name}"][f"{offset_row.partition}"] = offset_row.offset + 1