diff --git a/edx/analytics/tasks/common/spark.py b/edx/analytics/tasks/common/spark.py
index 63eb311024..a9039b6c57 100644
--- a/edx/analytics/tasks/common/spark.py
+++ b/edx/analytics/tasks/common/spark.py
@@ -197,6 +197,10 @@ class EventLogSelectionMixinSpark(EventLogSelectionDownstreamMixin):
     Extract events corresponding to a specified time interval.
     """
 
+    direct_eventlogs_processing = luigi.BoolParameter(
+        description='Whether or not to process event log source directly with spark',
+        default=False
+    )
     def __init__(self, *args, **kwargs):
         """
         Call path selection task to get list of log files matching the pattern
@@ -242,33 +246,34 @@ def get_log_schema(self):
         return event_log_schema
 
     def get_input_source(self, *args):
-        manifest_path = self.get_config_from_args('manifest_path', *args, default_value='')
-        targets = PathSelectionTaskSpark(
+        return PathSelectionByDateIntervalTask(
             source=self.source,
             interval=self.interval,
             pattern=self.pattern,
-            date_pattern=self.date_pattern,
-            manifest_id=self.manifest_id,
-            manifest_dir=manifest_path,
+            date_pattern=self.date_pattern
         ).output()
-        if len(targets) and 'manifest' in targets[0].path:
-            # Reading manifest as rdd with spark is alot faster as compared to hadoop.
-            # Currently, we're getting only 1 manifest file per request, so we will create a single rdd from it.
-            # If there are multiple manifest files, each file can be read as rdd and then union it with other manifest rdds
-            self.log.warn("PYSPARK LOGGER: Reading manifest file :: {} ".format(targets[0].path))
-            source_rdd = self._spark.sparkContext.textFile(targets[0].path)
-            broadcast_value = self._spark.sparkContext.broadcast(source_rdd.collect())
-        else:
-            self.log.warn("PYSPARK LOGGER: Reading normal targets")
-            broadcast_value = self._spark.sparkContext.broadcast([target.path for target in targets])
-        return broadcast_value
 
     def get_event_log_dataframe(self, spark, *args, **kwargs):
         from pyspark.sql.functions import to_date, udf, struct, date_format
-        dataframe = spark.read.format('json').load(
-            self.get_input_source(*args).value,
-            schema=self.get_log_schema()
-        )
+        schema = self.get_log_schema()
+        if self.direct_eventlogs_processing:
+            self.log.warn("\nPYSPARK => Processing event log source directly\n")
+            event_log_source = self.get_config_from_args('event_log_source', *args, default_value=None)
+            if event_log_source is not None:
+                event_log_source = json.loads(event_log_source)
+            self.log.warn("\nPYSPARK => Event log source : {}\n".format(event_log_source))
+            dataframe = spark.read.format('json').load(event_log_source[0], schema=self.get_log_schema())
+            source_list_count = len(event_log_source)
+            if source_list_count > 1:
+                for k in range(1, source_list_count):
+                    dataframe = dataframe.union(
+                        spark.read.format('json').load(event_log_source[k], schema=self.get_log_schema())
+                    )
+        else:
+            self.log.warn("\nPYSPARK => Processing path selection output\n")
+            input_source = self.get_input_source(*args)
+            path_targets = [target.path for target in input_source]
+            dataframe = spark.read.format('json').load(path_targets, schema=self.get_log_schema())
         dataframe = dataframe.filter(dataframe['time'].isNotNull()) \
             .withColumn('event_date', date_format(to_date(dataframe['time']), 'yyyy-MM-dd'))
         dataframe = dataframe.filter(
@@ -310,26 +315,6 @@ def conf(self):
         """
         return self._dict_config(self.spark_conf)
 
-    @property
-    def manifest_id(self):
-        params = {
-            'source': self.source,
-            'interval': self.interval,
-            'pattern': self.pattern,
-            'date_pattern': self.date_pattern,
-            'spark': 'for_some_difference_with_hadoop_manifest'
-        }
-        return str(hash(frozenset(params.items()))).replace('-', 'n')
-
-    def get_manifest_path(self, *args):
-        manifest_path = self.get_config_from_args('manifest_path', *args, default_value='')
-        return get_target_from_url(
-            url_path_join(
-                manifest_path,
-                self.manifest_id + '.manifest'
-            )
-        )
-
     def spark_job(self):
         """
         Spark code for the job
diff --git a/edx/analytics/tasks/insights/location_per_course.py b/edx/analytics/tasks/insights/location_per_course.py
index 1641ecd8d4..2ad1241335 100644
--- a/edx/analytics/tasks/insights/location_per_course.py
+++ b/edx/analytics/tasks/insights/location_per_course.py
@@ -219,7 +219,7 @@ def run(self):
     def get_luigi_configuration(self):
         options = {}
         config = luigi.configuration.get_config()
-        options['manifest_path'] = config.get('manifest', 'path', '')
+        options['event_log_source'] = config.get('event-logs', 'source', '')
         return options
 
     def spark_job(self, *args):
@@ -246,7 +246,7 @@ def spark_job(self, *args):
                 WHERE rank = 1
                 """
         result = self._spark.sql(query)
-        result.coalesce(4).write.partitionBy('dt').csv(self.output_dir().path, mode='append', sep='\t')
+        result.coalesce(2).write.partitionBy('dt').csv(self.output_dir().path, mode='append', sep='\t')
 
 
 class LastCountryOfUserDownstreamMixin(