diff --git a/openwpm_utils/s3.py b/openwpm_utils/s3.py
index b533e03..5654ba8 100644
--- a/openwpm_utils/s3.py
+++ b/openwpm_utils/s3.py
@@ -8,68 +8,7 @@
 from pyarrow.filesystem import S3FSWrapper  # noqa
 from pyspark.sql import SQLContext
 
-
-class PySparkS3Dataset(object):
-    def __init__(self, spark_context, s3_directory,
-                 s3_bucket='openwpm-crawls'):
-        """Helper class to load OpenWPM datasets from S3 using PySpark
-
-        Parameters
-        ----------
-        spark_context
-            Spark context. In databricks, this is available via the `sc`
-            variable.
-        s3_directory : string
-            Directory within the S3 bucket in which the dataset is saved.
-        s3_bucket : string, optional
-            The bucket name on S3. Defaults to `openwpm-crawls`.
-        """
-        self._s3_bucket = s3_bucket
-        self._s3_directory = s3_directory
-        self._spark_context = spark_context
-        self._sql_context = SQLContext(spark_context)
-        self._s3_table_loc = "s3a://%s/%s/visits/%%s/" % (
-            s3_bucket, s3_directory)
-        self._s3_content_loc = "s3a://%s/%s/content/%%s.gz" % (
-            s3_bucket, s3_directory)
-
-    def read_table(self, table_name, columns=None):
-        """Read `table_name` from OpenWPM dataset into a pyspark dataframe.
-
-        Parameters
-        ----------
-        table_name : string
-            OpenWPM table to read
-        columns : list of strings
-            The set of columns to filter the parquet dataset by
-        """
-        table = self._sql_context.read.parquet(self._s3_table_loc % table_name)
-        if columns is not None:
-            return table.select(columns)
-        return table
-
-    def read_content(self, content_hash):
-        """Read the content corresponding to `content_hash`.
-
-        NOTE: This can only be run in the driver process since it requires
-              access to the spark context
-        """
-        return self._spark_context.textFile(
-            self._s3_content_loc % content_hash)
-
-    def collect_content(self, content_hash, beautify=False):
-        """Collect content for `content_hash` to driver
-
-        NOTE: This can only be run in the driver process since it requires
-              access to the spark context
-        """
-        content = ''.join(self.read_content(content_hash).collect())
-        if beautify:
-            return jsbeautifier.beautify(content)
-        return content
-
-
-class S3Dataset(object):
+class S3Dataset:
     def __init__(self, s3_directory, s3_bucket='openwpm-crawls'):
         """Helper class to load OpenWPM datasets from S3 using pandas
 
@@ -134,3 +73,42 @@ def collect_content(self, content_hash, beautify=False):
             except IndexError:
                 pass
         return content
+
+class PySparkS3Dataset(S3Dataset):
+    def __init__(self, spark_context, s3_directory,
+                 s3_bucket='openwpm-crawls'):
+        """Helper class to load OpenWPM datasets from S3 using PySpark
+
+        Parameters
+        ----------
+        spark_context
+            Spark context. In databricks, this is available via the `sc`
+            variable.
+        s3_directory : string
+            Directory within the S3 bucket in which the dataset is saved.
+        s3_bucket : string, optional
+            The bucket name on S3. Defaults to `openwpm-crawls`.
+        """
+        self._s3_bucket = s3_bucket
+        self._s3_directory = s3_directory
+        self._spark_context = spark_context
+        self._sql_context = SQLContext(spark_context)
+        self._s3_table_loc = "s3a://%s/%s/visits/%%s/" % (
+            s3_bucket, s3_directory)
+        self._s3_content_loc = "s3a://%s/%s/content/%%s.gz" % (
+            s3_bucket, s3_directory)
+
+    def read_table(self, table_name, columns=None):
+        """Read `table_name` from OpenWPM dataset into a pyspark dataframe.
+
+        Parameters
+        ----------
+        table_name : string
+            OpenWPM table to read
+        columns : list of strings
+            The set of columns to filter the parquet dataset by
+        """
+        table = self._sql_context.read.parquet(self._s3_table_loc % table_name)
+        if columns is not None:
+            return table.select(columns)
+        return table