diff --git a/datalab/datalab_session/data_operations/stacking.py b/datalab/datalab_session/data_operations/stacking.py
new file mode 100644
index 0000000..96d2a36
--- /dev/null
+++ b/datalab/datalab_session/data_operations/stacking.py
@@ -0,0 +1,70 @@
+import logging
+
+import numpy as np
+
+from datalab.datalab_session.data_operations.data_operation import BaseDataOperation
+from datalab.datalab_session.exceptions import ClientAlertException
+from datalab.datalab_session.file_utils import create_fits, stack_arrays, create_jpgs
+from datalab.datalab_session.s3_utils import save_fits_and_thumbnails
+
+log = logging.getLogger()
+log.setLevel(logging.INFO)
+
+
+class Median(BaseDataOperation):
+    
+    @staticmethod
+    def name():
+        return 'Stacking'
+    
+    @staticmethod
+    def description():
+        return """The stacking operation takes in 2..n input images and adds the values pixel-by-pixel.
+
+The output is a stacked image for the n input images. This operation is commonly used for improving signal to noise."""
+
+    @staticmethod
+    def wizard_description():
+        description = {
+            'name': Median.name(),
+            'description': Median.description(),
+            'category': 'image',
+            'inputs': {
+                'input_files': {
+                    'name': 'Input Files',
+                    'description': 'The input files to operate on',
+                    'type': 'file',
+                    'minimum': 1,
+                    'maximum': 999
+                }
+            }
+        }
+        return description
+
+    def operate(self):
+
+        input_files = self.input_data.get('input_files', [])
+
+        if len(input_files) <= 1:
+            raise ClientAlertException('Stack needs at least 2 files')
+
+        log.info(f'Executing stacking operation on {len(input_files)} files')
+
+        image_data_list = self.get_fits_npdata(input_files, percent=0.4, cur_percent=0.0)
+
+        stacked_data = stack_arrays(image_data_list)
+
+        # using the numpy library's sum method
+        stacked_sum = np.sum(stacked_data, axis=2)
+
+        fits_file = create_fits(self.cache_key, stacked_sum)
+
+        large_jpg_path, small_jpg_path = create_jpgs(self.cache_key, fits_file)
+
+        output_file = save_fits_and_thumbnails(self.cache_key, fits_file, large_jpg_path, small_jpg_path)
+
+        output =  {'output_files': [output_file]}
+
+        self.set_percent_completion(1.0)
+        self.set_output(output)
+        log.info(f'Stacked output: {self.get_output()}')