Use DODataset to download a dataset from DO

cartoframes/data/observatory/catalog/entity.py

@@ -3,6 +3,7 @@
 from abc import ABC
 
 from ...clients.bigquery_client import BigQueryClient
+from carto.do_dataset import DODataset
 from ....utils.logger import log
 from ....exceptions import DOError
 
@@ -126,35 +127,21 @@ def _download(self, credentials, file_path=None, limit=None, order_by=None):
         if not self._is_available_in('bq'):
             raise DOError('{} is not ready for Download. Please, contact us for more information.'.format(self))
 
-        bq_client = _get_bigquery_client(credentials)
-
-        full_remote_table_name = self._get_remote_full_table_name(
-            bq_client.bq_project,
-            bq_client.bq_dataset,
-            bq_client.bq_public_project
-        )
-
-        project, dataset, table = full_remote_table_name.split('.')
-
-        column_names = bq_client.get_table_column_names(project, dataset, table)
-
-        query = 'SELECT * FROM `{}`'.format(full_remote_table_name)
-        if order_by:
-            query = '{} ORDER BY {}'.format(query, order_by)
-        if limit:
-            query = '{} LIMIT {}'.format(query, limit)
-
-        job = bq_client.query(query)
-
+        auth_client = credentials.get_api_key_auth_client()
+        rows = DODataset(auth_client=auth_client).name(self.id).download_stream(limit=limit, order_by=order_by)
         if file_path:
-            bq_client.download_to_file(job, file_path, column_names=column_names)
+            with open(file_path, 'w') as csvfile:
+                for row in rows:
+                    csvfile.write(row.decode('utf-8'))
+
             log.info('Data saved: {}'.format(file_path))
             if self.__class__.__name__ == 'Dataset':
                 log.info(_DATASET_READ_MSG.format(file_path))
             elif self.__class__.__name__ == 'Geography':
                 log.info(_GEOGRAPHY_READ_MSG.format(file_path))
         else:
-            return bq_client.download_to_dataframe(job)
+            dataframe = pd.read_csv(rows)
+            return dataframe
 
     def _is_available_in(self, platform=_PLATFORM_BQ):
         return self.data['available_in'] and platform in self.data['available_in']