From 0ab9b996c2fbe7ab8db9125941bd8fe39bd5114b Mon Sep 17 00:00:00 2001
From: rxu17 <26471741+rxu17@users.noreply.github.com>
Date: Thu, 27 Apr 2023 12:31:45 -0700
Subject: [PATCH] [ETL-409] Create python comparison script (#42)

* update code to use datacompy as main comparison tool
* add comparison report for edge scenarios like no data types in common, dups
* add function for converting csv to text for s3.put_object, add tests for getting all row diff, add linting, adjust function to output datacompy.Compare object to allow for more logs
* use resolvedoptions from aws glue, adjust all args and args validation func, remove unused lib
* add logging for memory for datasets
---------

Co-authored-by: Rixing Xu <rxu@w197.local>
---
 Pipfile                                   |   3 +
 Pipfile.lock                              | 656 ++++++++++++++--------
 src/glue/jobs/compare_parquet_datasets.py | 634 +++++++++++++++++++++
 tests/conftest.py                         | 258 +++++++++
 tests/test_compare_parquet_datasets.py    | 580 +++++++++++++++++++
 5 files changed, 1897 insertions(+), 234 deletions(-)
 create mode 100644 src/glue/jobs/compare_parquet_datasets.py
 create mode 100644 tests/conftest.py
 create mode 100644 tests/test_compare_parquet_datasets.py

diff --git a/Pipfile b/Pipfile
index 627ee0eb..93675b86 100644
--- a/Pipfile
+++ b/Pipfile
@@ -13,3 +13,6 @@ pre-commit = "*"
 sceptre = ">=3.2.0"
 sceptre-sam-handler = "*"
 synapseclient = "~=2.7"
+pandas = "<1.5"
+moto = "~=4.1"
+datacompy = "~=0.8"
diff --git a/Pipfile.lock b/Pipfile.lock
index 497c97af..7e1a650a 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,11 +1,11 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "ace3ba1c990fec71547a4f855cbcbc74a7550ad007e064a32d72b483b570e9ef"
+            "sha256": "37dad20fcae1538630a2efa8b154ad4caf220b2a1ad870cd8a887e92adf9fd53"
         },
         "pipfile-spec": 6,
         "requires": {
-            "python_version": "3.10"
+            "python_version": "3.9"
         },
         "sources": [
             {
@@ -27,19 +27,19 @@
         },
         "boto3": {
             "hashes": [
-                "sha256:bbb426a9b3afd3ccbac25e03b215d79e90b4c47905b1b08b3b9d86fc74096974",
-                "sha256:c92dd0fde7839c0ca9c16a989d67ceb7f80f53de19f2b087fd1182f2af41b2ae"
+                "sha256:b10324d452fe677d6e1005b650ae11158cf21310e0c0062b00d184b352f4196b",
+                "sha256:e32977256470b4de3a25a43acdf6c8e6375e762656ace292ad2ceba446203615"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==1.26.68"
+            "version": "==1.26.114"
         },
         "botocore": {
             "hashes": [
-                "sha256:08fa8302a22553e69b70b1de2cc8cec61a3a878546658d091473e13d5b9d2ca4",
-                "sha256:8f5cb96dc0862809d29fe512087c77c15fe6328a2d8238f0a96cccb6eb77ec12"
+                "sha256:aeacb03303d6babb0490247158647346d27900fcf89bfb9713db41f1ce1cc93f",
+                "sha256:dd61f445eb53fe906dbde86405915cce61ffa13824a1bb7b826bfc7869a3e628"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==1.29.68"
+            "version": "==1.29.114"
         },
         "certifi": {
             "hashes": [
@@ -49,6 +49,75 @@
             "markers": "python_version >= '3.6'",
             "version": "==2022.12.7"
         },
+        "cffi": {
+            "hashes": [
+                "sha256:00a9ed42e88df81ffae7a8ab6d9356b371399b91dbdf0c3cb1e84c03a13aceb5",
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+                "sha256:04ed324bda3cda42b9b695d51bb7d54b680b9719cfab04227cdd1e04e5de3104",
+                "sha256:0e2642fe3142e4cc4af0799748233ad6da94c62a8bec3a6648bf8ee68b1c7426",
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+                "sha256:198caafb44239b60e252492445da556afafc7d1e3ab7a1fb3f0584ef6d742375",
+                "sha256:1e74c6b51a9ed6589199c787bf5f9875612ca4a8a0785fb2d4a84429badaf22a",
+                "sha256:2012c72d854c2d03e45d06ae57f40d78e5770d252f195b93f581acf3ba44496e",
+                "sha256:21157295583fe8943475029ed5abdcf71eb3911894724e360acff1d61c1d54bc",
+                "sha256:2470043b93ff09bf8fb1d46d1cb756ce6132c54826661a32d4e4d132e1977adf",
+                "sha256:285d29981935eb726a4399badae8f0ffdff4f5050eaa6d0cfc3f64b857b77185",
+                "sha256:30d78fbc8ebf9c92c9b7823ee18eb92f2e6ef79b45ac84db507f52fbe3ec4497",
+                "sha256:320dab6e7cb2eacdf0e658569d2575c4dad258c0fcc794f46215e1e39f90f2c3",
+                "sha256:33ab79603146aace82c2427da5ca6e58f2b3f2fb5da893ceac0c42218a40be35",
+                "sha256:3548db281cd7d2561c9ad9984681c95f7b0e38881201e157833a2342c30d5e8c",
+                "sha256:3799aecf2e17cf585d977b780ce79ff0dc9b78d799fc694221ce814c2c19db83",
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+                "sha256:db0fbb9c62743ce59a9ff687eb5f4afbe77e5e8403d6697f7446e5f609976f76",
+                "sha256:dd86c085fae2efd48ac91dd7ccffcfc0571387fe1193d33b6394db7ef31fe2a4",
+                "sha256:e00b098126fd45523dd056d2efba6c5a63b71ffe9f2bbe1a4fe1716e1d0c331e",
+                "sha256:e229a521186c75c8ad9490854fd8bbdd9a0c9aa3a524326b55be83b54d4e0ad9",
+                "sha256:e263d77ee3dd201c3a142934a086a4450861778baaeeb45db4591ef65550b0a6",
+                "sha256:ed9cb427ba5504c1dc15ede7d516b84757c3e3d7868ccc85121d9310d27eed0b",
+                "sha256:fa6693661a4c91757f4412306191b6dc88c1703f780c8234035eac011922bc01",
+                "sha256:fcd131dd944808b5bdb38e6f5b53013c5aa4f334c5cad0c72742f6eba4b73db0"
+            ],
+            "version": "==1.15.1"
+        },
         "cfgv": {
             "hashes": [
                 "sha256:c6a0883f3917a037485059700b9e75da2464e6c27051014ad85ba6aaa5884426",
@@ -66,96 +135,84 @@
         },
         "charset-normalizer": {
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@@ -173,6 +230,39 @@
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             "version": "==0.4.6"
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@@ -189,6 +279,13 @@
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@@ -198,27 +295,27 @@
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@@ -230,11 +327,11 @@
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@@ -331,6 +428,14 @@
             "markers": "python_version >= '3.7'",
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@@ -349,37 +454,37 @@
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-            "version": "==1.24.2"
+            "version": "==1.24.1"
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         "ordered-set": {
             "hashes": [
@@ -397,13 +502,40 @@
             "markers": "python_version >= '3.6'",
             "version": "==21.3"
         },
+        "pandas": {
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+                "sha256:a981cfabf51c318a562deb4ae7deec594c07aee7cf18b4594a92c23718ec8275",
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+                "sha256:c4bb8b0ab9f94207d07e401d24baebfc63057246b1a5e0cd9ee50df85a656871",
+                "sha256:ce35f947202b0b99c660221d82beb91d2e6d553d55a40b30128204e3e2c63848",
+                "sha256:d0022fe6a313df1c4869b5edc012d734c6519a6fffa3cf70930f32e6a1078e49",
+                "sha256:e7cc960959be28d064faefc0cb2aef854d46b827c004ebea7e79b5497ed83e7d",
+                "sha256:ee6f1848148ed3204235967613b0a32be2d77f214e9623f554511047705c1e04"
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+            "index": "pypi",
+            "version": "==1.4.4"
+        },
         "platformdirs": {
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-                "sha256:b1d5eb14f221506f50d6604a561f4c5786d9e80355219694a1b244bcd96f4567"
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             "markers": "python_version >= '3.7'",
-            "version": "==3.0.0"
+            "version": "==3.2.0"
         },
         "pluggy": {
             "hashes": [
@@ -415,11 +547,11 @@
         },
         "pre-commit": {
             "hashes": [
-                "sha256:9e3255edb0c9e7fe9b4f328cb3dc86069f8fdc38026f1bf521018a05eaf4d67b",
-                "sha256:bc4687478d55578c4ac37272fe96df66f73d9b5cf81be6f28627d4e712e752d5"
+                "sha256:0b4210aea813fe81144e87c5a291f09ea66f199f367fa1df41b55e1d26e1e2b4",
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-            "version": "==3.0.4"
+            "version": "==3.2.2"
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         "pyarrow": {
             "hashes": [
@@ -452,6 +584,13 @@
             "index": "pypi",
             "version": "==11.0.0"
         },
+        "pycparser": {
+            "hashes": [
+                "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9",
+                "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"
+            ],
+            "version": "==2.21"
+        },
         "pyparsing": {
             "hashes": [
                 "sha256:2b020ecf7d21b687f219b71ecad3631f644a47f01403fa1d1036b0c6416d70fb",
@@ -495,11 +634,11 @@
         },
         "pytest": {
             "hashes": [
-                "sha256:c7c6ca206e93355074ae32f7403e8ea12163b1163c976fee7d4d84027c162be5",
-                "sha256:d45e0952f3727241918b8fd0f376f5ff6b301cc0777c6f9a556935c92d8a7d42"
+                "sha256:3799fa815351fea3a5e96ac7e503a96fa51cc9942c3753cda7651b93c1cfa362",
+                "sha256:434afafd78b1d78ed0addf160ad2b77a30d35d4bdf8af234fe621919d9ed15e3"
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             "index": "pypi",
-            "version": "==7.2.1"
+            "version": "==7.3.1"
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         "python-dateutil": {
             "hashes": [
@@ -509,6 +648,13 @@
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
             "version": "==2.8.2"
         },
+        "pytz": {
+            "hashes": [
+                "sha256:1d8ce29db189191fb55338ee6d0387d82ab59f3d00eac103412d64e0ebd0c588",
+                "sha256:a151b3abb88eda1d4e34a9814df37de2a80e301e68ba0fd856fb9b46bfbbbffb"
+            ],
+            "version": "==2023.3"
+        },
         "pyyaml": {
             "hashes": [
                 "sha256:08682f6b72c722394747bddaf0aa62277e02557c0fd1c42cb853016a38f8dedf",
@@ -552,6 +698,14 @@
             "markers": "python_version >= '3.7' and python_version < '4'",
             "version": "==2.28.2"
         },
+        "responses": {
+            "hashes": [
+                "sha256:8a3a5915713483bf353b6f4079ba8b2a29029d1d1090a503c70b0dc5d9d0c7bd",
+                "sha256:c4d9aa9fc888188f0c673eff79a8dadbe2e75b7fe879dc80a221a06e0a68138f"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==0.23.1"
+        },
         "s3transfer": {
             "hashes": [
                 "sha256:06176b74f3a15f61f1b4f25a1fc29a4429040b7647133a463da8fa5bd28d5ecd",
@@ -562,18 +716,18 @@
         },
         "sceptre": {
             "hashes": [
-                "sha256:03f4e40b757728de157e0bf1dd4e8c7f49582207bd728f46c6563a054bdee116",
-                "sha256:19ebea9cbb7abe0dd28d86f42c68e63f3cd0833a46afd87e9d618b6ff80fe35a"
+                "sha256:b8cd3445e58d0f423321d517de28df98a1896413c9f6d539da7b55701a103be1",
+                "sha256:df22e4bb2845c3a5494a90ce70387d0125ffd624fe203976c22d592158bf645c"
             ],
             "index": "pypi",
-            "version": "==3.3.0"
+            "version": "==4.0.2"
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         "sceptre-cmd-resolver": {
             "hashes": [
-                "sha256:4cc7409ee43923bc97dc60b6d3bec2e129876e1ab9ca0ba656d4f8c40b2ac87b",
-                "sha256:ff83298ae86a51df150de28cd17c3754c64aad2bc7813c3095cc5cfc7fad89f1"
+                "sha256:155c47e2f4f55c7b6eb64bfe8760174701442ecaddba1a6f5cb7715a1c95be99",
+                "sha256:eea8ce4cfcd9199f726b4280e7e35923c9d4ea5d75cbe4a8ee78c0d6d2996d09"
             ],
-            "version": "==1.2.1"
+            "version": "==2.0.0"
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         "sceptre-file-resolver": {
             "hashes": [
@@ -584,19 +738,19 @@
         },
         "sceptre-sam-handler": {
             "hashes": [
-                "sha256:556be971ecf66c35f00ac14289671927432b418f1710fb92601f35c2d12d954c",
-                "sha256:a4805fca3e8663d8a1d6d0c794c6de3de19b54b232274bf30f0c56fddcb97208"
+                "sha256:ba50af0b09807c7dbff6471bb07c4dceda46027d693a7f2db7adf915fba9205b",
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             "index": "pypi",
-            "version": "==0.3.1"
+            "version": "==1.0.0"
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-                "sha256:b440ee5f7e607bb8c9de15259dba2583dd41a38879a7abc1d43a71c59524da48"
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-            "version": "==67.2.0"
+            "version": "==67.6.1"
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         "six": {
             "hashes": [
@@ -622,99 +776,133 @@
             "markers": "python_version < '3.11'",
             "version": "==2.0.1"
         },
+        "types-pyyaml": {
+            "hashes": [
+                "sha256:5aed5aa66bd2d2e158f75dda22b059570ede988559f030cf294871d3b647e3e8",
+                "sha256:c51b1bd6d99ddf0aa2884a7a328810ebf70a4262c292195d3f4f9a0005f9eeb6"
+            ],
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-            "version": "==1.26.14"
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+            "version": "==20.21.0"
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+                "sha256:f2e69b3ed24544b0d3dbe2c5c0ba5153ce50dcebb576fdc4696d52aa22db6034",
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+                "sha256:fd69666217b62fa5d7c6aa88e507493a34dec4fa20c5bd925e4bc12fce586639"
             ],
             "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
-            "version": "==1.14.1"
+            "version": "==1.15.0"
+        },
+        "xmltodict": {
+            "hashes": [
+                "sha256:341595a488e3e01a85a9d8911d8912fd922ede5fecc4dce437eb4b6c8d037e56",
+                "sha256:aa89e8fd76320154a40d19a0df04a4695fb9dc5ba977cbb68ab3e4eb225e7852"
+            ],
+            "markers": "python_version >= '3.4'",
+            "version": "==0.13.0"
         },
         "zipp": {
             "hashes": [
-                "sha256:23f70e964bc11a34cef175bc90ba2914e1e4545ea1e3e2f67c079671883f9cb6",
-                "sha256:e8b2a36ea17df80ffe9e2c4fda3f693c3dad6df1697d3cd3af232db680950b0b"
+                "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b",
+                "sha256:48904fc76a60e542af151aded95726c1a5c34ed43ab4134b597665c86d7ad556"
             ],
             "markers": "python_version >= '3.7'",
-            "version": "==3.13.0"
+            "version": "==3.15.0"
         }
     }
 }
diff --git a/src/glue/jobs/compare_parquet_datasets.py b/src/glue/jobs/compare_parquet_datasets.py
new file mode 100644
index 00000000..1627ad02
--- /dev/null
+++ b/src/glue/jobs/compare_parquet_datasets.py
@@ -0,0 +1,634 @@
+import argparse
+from io import StringIO
+import json
+import logging
+import os
+import sys
+
+from awsglue.utils import getResolvedOptions
+import boto3
+import datacompy
+import pandas as pd
+from pyarrow import fs
+import pyarrow.parquet as pq
+
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+INDEX_FIELD_MAP = {
+    "dataset_enrolledparticipants": ["ParticipantIdentifier"],
+    "dataset_fitbitprofiles": ["ParticipantIdentifier", "ModifiedDate"],
+    "dataset_fitbitdevices": ["ParticipantIdentifier", "Date"],
+    "dataset_fitbitactivitylogs": ["LogId"],
+    "dataset_fitbitdailydata": ["ParticipantIdentifier", "Date"],
+    "dataset_fitbitintradaycombined": ["ParticipantIdentifier", "Type", "DateTime"],
+    "dataset_fitbitrestingheartrates": ["ParticipantIdentifier", "Date"],
+    "dataset_fitbitsleeplogs": ["LogId"],
+    "dataset_healthkitv2characteristics": ["HealthKitCharacteristicKey"],
+    "dataset_healthkitv2samples": ["HealthKitSampleKey"],
+    "dataset_healthkitv2samples_deleted": ["HealthKitSampleKey"],
+    "dataset_healthkitv2heartbeat": ["HealthKitHeartbeatSampleKey"],
+    "dataset_healthkitv2statistics": ["HealthKitStatisticKey"],
+    "dataset_healthkitv2clinicalrecords": ["HealthKitClinicalRecordKey"],
+    "dataset_healthkitv2electrocardiogram": ["HealthKitECGSampleKey"],
+    "dataset_healthkitv2workouts": ["HealthKitWorkoutKey"],
+    "dataset_healthkitv2activitysummaries": ["HealthKitActivitySummaryKey"],
+    "dataset_googlefitsamples": ["GoogleFitSampleKey"],
+    "dataset_symptomlog": ["DataPointKey"],
+}
+
+
+def read_args() -> dict:
+    """Returns the specific params that our code needs to run"""
+    args = getResolvedOptions(
+        sys.argv, ["data-type", "staging-namespace", "main-namespace", "parquet-bucket"]
+    )
+    for arg in args:
+        validate_args(args[arg])
+    return args
+
+
+def validate_args(value: str) -> None:
+    """Checks to make sure none of the input command line arguments are empty strings
+
+    Args:
+        value (str): the value of the command line argument parsed by argparse
+
+    Raises:
+        ValueError: when value is an empty string
+    """
+    if value == "":
+        raise ValueError("Argument value cannot be an empty string")
+    else:
+        return None
+
+
+def get_s3_file_key_for_comparison_results(
+    staging_namespace: str, data_type: str = None, file_name: str = ""
+) -> str:
+    """Gets the s3 file key for saving the comparison results to
+    Note that file_name should contain the file extension.
+
+    NOTE: When using s3.put_object, if the bucket name is part of the the
+    Key parameter where we put the filepath to the file to save, it will
+    just assume it's another folder and will create another folder inside the
+    bucket"""
+    s3_folder_prefix = os.path.join(staging_namespace, "comparison_result")
+    if file_name.endswith(".csv") or file_name.endswith(".txt"):
+        if data_type:
+            return os.path.join(s3_folder_prefix, data_type, f"{file_name}")
+        else:
+            return os.path.join(s3_folder_prefix, f"{file_name}")
+    else:
+        raise TypeError(
+            f"file_name {file_name} should contain one of the following file extensions: [.txt, .csv]"
+        )
+
+
+def get_parquet_dataset_s3_path(parquet_bucket: str, namespace: str, data_type: str):
+    """Gets the s3 filepath to the parquet datasets"""
+    return os.path.join("s3://", parquet_bucket, namespace, "parquet", data_type)
+
+
+def get_duplicated_columns(dataset: pd.DataFrame) -> list:
+    """Gets a list of duplicated columns in a dataframe"""
+    return dataset.columns[dataset.columns.duplicated()].tolist()
+
+
+def has_common_cols(staging_dataset: pd.DataFrame, main_dataset: pd.DataFrame) -> list:
+    """Gets the list of common columns between two dataframes
+    TODO: Could look into depreciating this and using datacompy.intersect_columns function"""
+    common_cols = staging_dataset.columns.intersection(main_dataset.columns).tolist()
+    return common_cols != []
+
+
+def get_missing_cols(staging_dataset: pd.DataFrame, main_dataset: pd.DataFrame) -> list:
+    """Gets the list of missing columns present in main but not in staging
+    TODO: Could look into depreciating this and using datacompy.df2_unq_columns function"""
+    missing_cols = main_dataset.columns.difference(staging_dataset.columns).tolist()
+    return missing_cols
+
+
+def get_additional_cols(
+    staging_dataset: pd.DataFrame, main_dataset: pd.DataFrame
+) -> list:
+    """Gets the list of additional columns present in staging but not in main
+    TODO: Could look into depreciating this and using datacompy.df1_unq_columns function"""
+    add_cols = staging_dataset.columns.difference(main_dataset.columns).tolist()
+    return add_cols
+
+
+def convert_dataframe_to_text(dataset: pd.DataFrame) -> str:
+    """Converts a pandas DataFrame into a string to save as csv in S3
+
+    NOTE: This is because s3.put_object only allows bytes or string
+    objects when saving them to S3"""
+    csv_buffer = StringIO()
+    dataset.to_csv(csv_buffer)
+    csv_content = csv_buffer.getvalue()
+    return csv_content
+
+
+def get_S3FileSystem_from_session(
+    aws_session: boto3.session.Session,
+) -> fs.S3FileSystem:
+    """Gets a pyarrow S3 filesystem object given an
+    authenticated aws session with credentials
+
+    Args:
+        aws_session (boto3.session.Session): authenticated aws session
+
+    Returns:
+        fs.S3FileSystem: S3 filesystem object initiated from AWS credentials
+    """
+    session_credentials = aws_session.get_credentials()
+    s3_fs = fs.S3FileSystem(
+        access_key=session_credentials.access_key,
+        secret_key=session_credentials.secret_key,
+        session_token=session_credentials.token,
+    )
+    return s3_fs
+
+
+def get_parquet_dataset(
+    dataset_key: str, s3_filesystem: fs.S3FileSystem
+) -> pd.DataFrame:
+    """
+    Returns a Parquet dataset on S3 as a pandas dataframe
+
+    Args:
+        dataset_key (str): The URI of the parquet dataset.
+        s3_filesystem (S3FileSystem): A fs.S3FileSystem object
+
+    Returns:
+        pandas.DataFrame
+
+    TODO: Currently, internal pyarrow things like to_table as a
+    result of the read_table function below takes a while as the dataset
+    grows bigger. Could find a way to optimize that.
+    """
+    table_source = dataset_key.split("s3://")[-1]
+    parquet_dataset = pq.read_table(source=table_source, filesystem=s3_filesystem)
+    return parquet_dataset.to_pandas()
+
+
+def get_folders_in_s3_bucket(
+    s3: boto3.client, bucket_name: str, namespace: str
+) -> list:
+    """Gets the folders in the S3 bucket under the specific namespace
+
+    Args:
+        s3 (boto3.client): authenticated s3 client
+        bucket_name (str): name of the S3 bucket to look into
+        namespace (str): namespace of the path to look for folders in
+
+    Returns:
+        list: folder names inside S3 bucket
+    """
+
+    response = s3.list_objects_v2(
+        Bucket=bucket_name, Prefix=f"{namespace}/parquet/", Delimiter="/"
+    )
+    if "CommonPrefixes" in response.keys():
+        contents = response["CommonPrefixes"]
+        folders = [
+            os.path.normpath(content["Prefix"]).split(os.sep)[-1]
+            for content in contents
+        ]
+    else:
+        folders = []
+    return folders
+
+
+def get_duplicates(compare_obj: datacompy.Compare, namespace: str) -> pd.DataFrame:
+    """Uses the datacompy Compare object to get all duplicates for a given dataset
+    Args:
+        compare_obj (datacompy.Compare): compare object that was defined earlier
+        namespace (str): The dataset we want to get the duplicated rows from
+
+    Returns:
+        pd.DataFrame: All the duplicated rows
+    """
+    if namespace == "staging":
+        dup_rows = compare_obj.df1[
+            compare_obj.df1.duplicated(subset=compare_obj.join_columns)
+        ]
+    elif namespace == "main":
+        dup_rows = compare_obj.df2[
+            compare_obj.df2.duplicated(subset=compare_obj.join_columns)
+        ]
+    else:
+        raise KeyError("namespace can only be one of 'staging', 'main'")
+    return dup_rows
+
+
+def compare_row_diffs(compare_obj: datacompy.Compare, namespace: str) -> pd.DataFrame:
+    """Uses the datacompy Compare object to get all rows that are different
+        in each dataset
+    Args:
+        compare_obj (datacompy.Compare): compare object that was defined earlier
+        namespace (str): The dataset we want to get the rows that are different from
+
+    Returns:
+        pd.DataFrame: All the rows that's in one dataframe but not the other
+    """
+    if namespace == "staging":
+        columns = compare_obj.df1_unq_rows.columns
+        rows = compare_obj.df1_unq_rows.sample(compare_obj.df1_unq_rows.shape[0])[
+            columns
+        ]
+    elif namespace == "main":
+        columns = compare_obj.df2_unq_rows.columns
+        rows = compare_obj.df2_unq_rows.sample(compare_obj.df2_unq_rows.shape[0])[
+            columns
+        ]
+    else:
+        raise KeyError("namespace can only be one of 'staging', 'main'")
+    return rows
+
+
+def compare_column_names(
+    data_type: str, staging_dataset: pd.DataFrame, main_dataset: pd.DataFrame
+) -> list:
+    """This compares the column names between two datasets and outputs a
+    message if there are any differences"""
+    compare_msg = []
+    missing_cols = get_missing_cols(staging_dataset, main_dataset)
+    add_cols = get_additional_cols(staging_dataset, main_dataset)
+    if missing_cols:
+        compare_msg.append(
+            f"{data_type}: Staging dataset has the following missing columns:\n{str(missing_cols)}"
+        )
+    if add_cols:
+        compare_msg.append(
+            f"{data_type}: Staging dataset has the following additional columns:\n{str(add_cols)}"
+        )
+    return compare_msg
+
+
+def get_data_types_to_compare(
+    s3: boto3.client, bucket_name: str, staging_namespace: str, main_namespace: str
+) -> list:
+    """This gets the common data types to run the comparison of the parquet datasets from
+    the two namespaced paths on based on the folders in the s3 bucket"""
+    staging_datatype_folders = get_folders_in_s3_bucket(
+        s3, bucket_name, namespace=staging_namespace
+    )
+    main_datatype_folders = get_folders_in_s3_bucket(
+        s3, bucket_name, namespace=main_namespace
+    )
+    return list(set(staging_datatype_folders) & set(main_datatype_folders))
+
+
+def compare_dataset_data_types(
+    s3: boto3.client, bucket_name: str, staging_namespace: str, main_namespace: str
+) -> list:
+    """This looks at the current datatype folders in the S3 bucket between the
+    two namespaced paths and outputs a message if there are any differences
+    in the datatype folders"""
+    compare_msg = []
+    staging_datatype_folders = get_folders_in_s3_bucket(
+        s3, bucket_name, namespace=staging_namespace
+    )
+    main_datatype_folders = get_folders_in_s3_bucket(
+        s3, bucket_name, namespace=main_namespace
+    )
+    missing_datatypes = list(set(main_datatype_folders) - set(staging_datatype_folders))
+    add_datatypes = list(set(staging_datatype_folders) - set(main_datatype_folders))
+
+    if missing_datatypes:
+        compare_msg.append(
+            f"Staging dataset has the following missing data types: {str(missing_datatypes)}"
+        )
+
+    if add_datatypes:
+        compare_msg.append(
+            f"Staging dataset has the following additional data types: {str(add_datatypes)}"
+        )
+    return compare_msg
+
+
+def compare_datasets_and_output_report(
+    data_type: str,
+    staging_dataset: pd.DataFrame,
+    main_dataset: pd.DataFrame,
+    staging_namespace: str,
+    main_namespace: str,
+) -> datacompy.Compare:
+    """This method prints out a human-readable report summarizing and
+    sampling differences between datasets for the given data type.
+    A full list of comparisons can be found in the datacompy package site.
+
+    Returns:
+        datacompy.Compare: object containing
+
+    TODO: Look into using datacompy.SparkCompare as in the docs, it mentions
+    it works with data that is partitioned Parquet, CSV, or JSON files,
+    or Cerebro tables. This will also likely be necessary once we have
+    more data coming in over weeks and months
+    """
+    # there exists folders with data subtypes, but we want to just merge on
+    # the main level datatypes
+    main_data_type = f'dataset_{data_type.split("_")[1]}'
+    compare = datacompy.Compare(
+        df1=staging_dataset,
+        df2=main_dataset,
+        join_columns=INDEX_FIELD_MAP[main_data_type],
+        abs_tol=0,  # Optional, defaults to 0
+        rel_tol=0,  # Optional, defaults to 0
+        df1_name=staging_namespace,  # Optional, defaults to 'df1'
+        df2_name=main_namespace,  # Optional, defaults to 'df2'
+        cast_column_names_lower=False,
+    )
+    compare.matches(ignore_extra_columns=False)
+    return compare
+
+
+def add_additional_msg_to_comparison_report(
+    comparison_report: str, add_msgs: list, msg_type: str
+) -> str:
+    """This adds additional messages to the comparison report. Currently, this adds
+        messages that specify the names of columns that are different between the
+        two datasets
+
+    Args:
+        comparison_report (str): report generated using datacompy
+        add_msgs (list): list of additional messages to include at the bottom of the report
+        msg_type (str): category of message, current available ones are
+            ["column_name_diff", "data_type_diff"]
+
+    Returns:
+        str: updated comparison report with more specific messages
+    """
+    # does some formatting.
+    joined_add_msgs = "\n".join(add_msgs)
+    if msg_type == "column_name_diff":
+        updated_comparison_report = (
+            f"{comparison_report}"
+            f"Column Name Differences\n"
+            f"-----------------------\n\n{joined_add_msgs}"
+        )
+    elif msg_type == "data_type_diff":
+        updated_comparison_report = (
+            f"{comparison_report}"
+            f"Data Type Differences between the namespaces\n"
+            f"--------------------------------------------\n\n{joined_add_msgs}"
+        )
+    else:
+        raise ValueError(
+            "msg_type param must be one of 'column_name_diff', 'data_type_diff'"
+        )
+    return updated_comparison_report
+
+
+def is_valid_dataset(dataset: pd.DataFrame, namespace: str) -> dict:
+    """Checks whether the individual dataset is valid under the following criteria:
+        - no duplicated columns
+        - dataset is not empty (aka has columns)
+        before it can go through the comparison
+
+    Args:
+        dataset (pd.DataFrame): dataset to be validated
+        namespace (str): namespace for the dataset
+
+    Returns:
+        dict: containing boolean of the validation result and string message
+    """
+    # Check that datasets have no emptiness, duplicated columns, or have columns in common
+    if len(dataset.columns) == 0:
+        msg = f"{namespace} dataset has no data. Comparison cannot continue."
+        return {"result": False, "msg": msg}
+    elif get_duplicated_columns(dataset):
+        msg = (
+            f"{namespace} dataset has duplicated columns. Comparison cannot continue.\n"
+            f"Duplicated columns:{str(get_duplicated_columns(dataset))}"
+        )
+        return {"result": False, "msg": msg}
+    else:
+        msg = f"{namespace} dataset has been validated."
+        return {"result": True, "msg": msg}
+
+
+def compare_datasets_by_data_type(
+    parquet_bucket: str,
+    staging_namespace: str,
+    main_namespace: str,
+    s3_filesystem: fs.S3FileSystem,
+    data_type: str,
+) -> dict:
+    """This runs the bulk of the comparison functions from beginning to end by data type
+
+    Args:
+        parquet_bucket (str): name of the bucket containing the parquet datasets
+        staging_namespace (str): name of namespace containing the "new" data
+        main_namespace (str): name of namespace containing the "established" data
+        s3_filesystem (fs.S3FileSystem): filesystem instantiated by aws credentials
+        data_type (str): data type to be compared for the given datasets
+
+    Returns:
+        dict:
+            compare_obj: the datacompy.Compare obj on the two datasets
+            comparison_report:final report on the datasets for the given data type
+    """
+    header_msg = (
+        f"\n\nParquet Dataset Comparison running for Data Type: {data_type}"
+        f"\n-----------------------------------------------------------------\n\n"
+    )
+    staging_dataset = get_parquet_dataset(
+        dataset_key=get_parquet_dataset_s3_path(
+            parquet_bucket, staging_namespace, data_type
+        ),
+        s3_filesystem=s3_filesystem,
+    )
+    main_dataset = get_parquet_dataset(
+        dataset_key=get_parquet_dataset_s3_path(
+            parquet_bucket, main_namespace, data_type
+        ),
+        s3_filesystem=s3_filesystem,
+    )
+    # go through specific validation for each dataset prior to comparison
+    staging_is_valid_result = is_valid_dataset(staging_dataset, staging_namespace)
+    main_is_valid_result = is_valid_dataset(main_dataset, main_namespace)
+    if (
+        staging_is_valid_result["result"] == False
+        or main_is_valid_result["result"] == False
+    ):
+        comparison_report = (
+            f"{staging_is_valid_result['msg']}\n{main_is_valid_result['msg']}"
+        )
+        compare = None
+    # check that they have columns in common to compare
+    elif not has_common_cols(staging_dataset, main_dataset):
+        comparison_report = (
+            f"{staging_namespace} dataset and {main_namespace} have no columns in common."
+            f" Comparison cannot continue."
+        )
+        compare = None
+    else:
+        logger.info(
+            f"{staging_namespace} dataset memory usage:"
+            f"{staging_dataset.memory_usage(deep=True).sum()/1e+6} MB"
+        )
+        logger.info(
+            f"{main_namespace} dataset memory usage:"
+            f"{main_dataset.memory_usage(deep=True).sum()/1e+6} MB"
+        )
+        compare = compare_datasets_and_output_report(
+            data_type=data_type,
+            staging_dataset=staging_dataset,
+            main_dataset=main_dataset,
+            staging_namespace=staging_namespace,
+            main_namespace=main_namespace,
+        )
+
+        comparison_report = add_additional_msg_to_comparison_report(
+            compare.report(),
+            add_msgs=compare_column_names(data_type, staging_dataset, main_dataset),
+            msg_type="column_name_diff",
+        )
+    return {
+        "compare_obj": compare,
+        "comparison_report": f"{header_msg}{comparison_report}",
+    }
+
+
+def main():
+    args = read_args()
+    s3 = boto3.client("s3")
+    aws_session = boto3.session.Session(region_name="us-east-1")
+    fs = get_S3FileSystem_from_session(aws_session)
+    data_type = args["data_type"]
+
+    data_types_to_compare = get_data_types_to_compare(
+        s3,
+        args["parquet_bucket"],
+        main_namespace=args["main_namespace"],
+        staging_namespace=args["staging_namespace"],
+    )
+    data_types_diff = compare_dataset_data_types(
+        s3,
+        args["parquet_bucket"],
+        main_namespace=args["main_namespace"],
+        staging_namespace=args["staging_namespace"],
+    )
+    if data_types_to_compare:
+        logger.info(f"Running comparison report for {data_type}")
+        compare_dict = compare_datasets_by_data_type(
+            parquet_bucket=args["parquet_bucket"],
+            staging_namespace=args["staging_namespace"],
+            main_namespace=args["main_namespace"],
+            s3_filesystem=fs,
+            data_type=data_type,
+        )
+        # update comparison report with the data_type differences message
+        comparison_report = add_additional_msg_to_comparison_report(
+            compare_dict["comparison_report"],
+            add_msgs=data_types_diff,
+            msg_type="data_type_diff",
+        )
+        # save comparison report to report folder in staging namespace
+        s3.put_object(
+            Bucket=args["parquet_bucket"],
+            Key=get_s3_file_key_for_comparison_results(
+                staging_namespace=args["staging_namespace"],
+                data_type=data_type,
+                file_name="parquet_compare.txt",
+            ),
+            Body=comparison_report,
+        )
+        logger.info("Comparison report saved!")
+        # additional report print outs
+        compare = compare_dict["compare_obj"]
+        # TODO: Find out if pandas.to_csv, or direct write to S3
+        # is more efficient. s3.put_object is very slow and memory heavy
+        # esp. if using StringIO conversion
+
+        # print out all mismatch columns
+        mismatch_cols_report = compare.all_mismatch()
+        if not mismatch_cols_report.empty:
+            s3.put_object(
+                Bucket=args["parquet_bucket"],
+                Key=get_s3_file_key_for_comparison_results(
+                    staging_namespace=args["staging_namespace"],
+                    data_type=data_type,
+                    file_name="all_mismatch_cols.csv",
+                ),
+                Body=convert_dataframe_to_text(mismatch_cols_report),
+            )
+            logger.info("Mismatch columns saved!")
+        # print out all staging rows that are different to main
+        staging_rows_report = compare_row_diffs(compare, namespace="staging")
+        if not staging_rows_report.empty:
+            s3.put_object(
+                Bucket=args["parquet_bucket"],
+                Key=get_s3_file_key_for_comparison_results(
+                    staging_namespace=args["staging_namespace"],
+                    data_type=data_type,
+                    file_name="all_diff_staging_rows.csv",
+                ),
+                Body=convert_dataframe_to_text(staging_rows_report),
+            )
+            logger.info("Different staging dataset rows saved!")
+        # print out all main rows that are different to staging
+        main_rows_report = compare_row_diffs(compare, namespace="main")
+        if not main_rows_report.empty:
+            s3.put_object(
+                Bucket=args["parquet_bucket"],
+                Key=get_s3_file_key_for_comparison_results(
+                    staging_namespace=args["staging_namespace"],
+                    data_type=data_type,
+                    file_name="all_diff_main_rows.csv",
+                ),
+                Body=convert_dataframe_to_text(main_rows_report),
+            )
+            logger.info("Different main dataset rows saved!")
+
+        # print out all staging duplicated rows
+        staging_dups_report = get_duplicates(compare, namespace="staging")
+        if not staging_dups_report.empty:
+            s3.put_object(
+                Bucket=args["parquet_bucket"],
+                Key=get_s3_file_key_for_comparison_results(
+                    staging_namespace=args["staging_namespace"],
+                    data_type=data_type,
+                    file_name="all_dup_staging_rows.csv",
+                ),
+                Body=convert_dataframe_to_text(staging_dups_report),
+            )
+            logger.info("Staging dataset duplicates saved!")
+        # print out all main duplicated rows
+        main_dups_report = get_duplicates(compare, namespace="main")
+        if not main_dups_report.empty:
+            s3.put_object(
+                Bucket=args["parquet_bucket"],
+                Key=get_s3_file_key_for_comparison_results(
+                    staging_namespace=args["staging_namespace"],
+                    data_type=data_type,
+                    file_name="all_dup_main_rows.csv",
+                ),
+                Body=convert_dataframe_to_text(main_dups_report),
+            )
+            logger.info("Main dataset duplicates saved!")
+    else:
+        # update comparison report with the data_type differences message
+        comparison_report = add_additional_msg_to_comparison_report(
+            comparison_report,
+            add_msgs=data_types_diff,
+            msg_type="data_type_diff",
+        )
+        print(comparison_report)
+        s3.put_object(
+            Bucket=args["parquet_bucket"],
+            Key=get_s3_file_key_for_comparison_results(
+                staging_namespace=args["staging_namespace"],
+                data_type=None,
+                file_name="data_types_compare.txt",
+            ),
+            Body=comparison_report,
+        )
+        logger.info("Comparison report saved!")
+    return
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 00000000..faf4bd3f
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,258 @@
+import os
+from unittest import mock
+
+import boto3
+import pytest
+import pandas as pd
+from pyarrow import fs, parquet
+from moto import mock_s3
+
+
+@pytest.fixture(scope="function")
+def mock_aws_credentials():
+    """Mocked AWS Credentials for moto."""
+    os.environ["AWS_ACCESS_KEY_ID"] = "testing"
+    os.environ["AWS_SECRET_ACCESS_KEY"] = "testing"
+    os.environ["AWS_SECURITY_TOKEN"] = "testing"
+    os.environ["AWS_SESSION_TOKEN"] = "testing"
+    os.environ["AWS_DEFAULT_REGION"] = "us-east-1"
+
+
+@pytest.fixture(scope="function")
+def s3(mock_aws_credentials):
+    with mock_s3():
+        yield boto3.client("s3", region_name="us-east-1")
+
+
+@pytest.fixture(scope="function")
+def mock_aws_session(mock_aws_credentials):
+    with mock_s3():
+        yield boto3.session.Session(region_name="us-east-1")
+
+
+@pytest.fixture()
+def parquet_bucket_name():
+    yield "test-parquet-bucket"
+
+
+@pytest.fixture(scope="function")
+def mock_s3_filesystem(mock_aws_session):
+    with mock_s3():
+        session_credentials = mock_aws_session.get_credentials()
+        yield fs.S3FileSystem(
+            region="us-east-1",
+            access_key=session_credentials.access_key,
+            secret_key=session_credentials.secret_key,
+            session_token=session_credentials.token,
+        )
+
+
+@pytest.fixture(scope="function")
+def valid_staging_parquet_object(tmpdir_factory, valid_staging_dataset):
+    filename = str(tmpdir_factory.mktemp("data_folder").join("df.parquet"))
+    valid_staging_dataset.to_parquet(path=filename, engine="pyarrow")
+    data = parquet.read_table(filename)
+    yield data
+
+
+@pytest.fixture()
+def dataset_fixture(request):
+    """This allows us to use different fixtures for the same test"""
+    return request.getfixturevalue(request.param)
+
+
+@pytest.fixture()
+def valid_staging_dataset():
+    yield pd.DataFrame(
+        {
+            "LogId": [
+                "44984262767",
+                "46096730542",
+                "51739302864",
+            ],
+            "StartDate": [
+                "2021-12-24T14:27:39+00:00",
+                "2022-02-18T08:26:54+00:00",
+                "2022-10-28T11:58:50+00:00",
+            ],
+            "EndDate": [
+                "2021-12-24T14:40:27+00:00",
+                "2022-02-18T09:04:30+00:00",
+                "2022-10-28T12:35:38+00:00",
+            ],
+            "ActiveDuration": ["768000", "2256000", "2208000"],
+            "Calories": ["89", "473", "478"],
+        }
+    )
+
+
+@pytest.fixture()
+def valid_main_dataset():
+    yield pd.DataFrame(
+        {
+            "LogId": [
+                "44984262767",
+                "46096730542",
+                "51739302864",
+            ],
+            "StartDate": [
+                "2021-12-24T14:27:39+00:00",
+                "2022-02-18T08:26:54+00:00",
+                "2022-10-28T11:58:50+00:00",
+            ],
+            "EndDate": [
+                "2021-12-24T14:40:27+00:00",
+                "2022-02-18T09:04:30+00:00",
+                "2022-10-28T12:35:38+00:00",
+            ],
+            "ActiveDuration": ["768000", "2256000", "2208000"],
+            "Calories": ["89", "473", "478"],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_missing_cols():
+    yield pd.DataFrame(
+        {
+            "LogId": [
+                "44984262767",
+                "46096730542",
+                "51739302864",
+            ],
+            "ActiveDuration": ["768000", "2256000", "2208000"],
+            "Calories": ["89", "473", "478"],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_add_cols():
+    yield pd.DataFrame(
+        {
+            "LogId": [
+                "44984262767",
+                "46096730542",
+                "51739302864",
+            ],
+            "StartDate": [
+                "2021-12-24T14:27:39+00:00",
+                "2022-02-18T08:26:54+00:00",
+                "2022-10-28T11:58:50+00:00",
+            ],
+            "EndDate": [
+                "2021-12-24T14:40:27+00:00",
+                "2022-02-18T09:04:30+00:00",
+                "2022-10-28T12:35:38+00:00",
+            ],
+            "ActiveDuration": ["768000", "2256000", "2208000"],
+            "Calories": ["89", "473", "478"],
+            "AverageHeartRate": ["108", "151", "157"],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_no_common_cols():
+    yield pd.DataFrame(
+        {
+            "ParticipantIdentifier": [
+                "MDH-9352-3209",
+                "MDH-9352-3209",
+                "MDH-9352-3209",
+            ],
+            "Steps": ["866", "6074", "5744"],
+            "OriginalDuration": ["768000", "2256000", "2208000"],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_diff_data_type_cols():
+    yield pd.DataFrame(
+        {
+            "LogId": [
+                "44984262767",
+                "46096730542",
+                "51739302864",
+            ],
+            "StartDate": [
+                "2021-12-24T14:27:39+00:00",
+                "2022-02-18T08:26:54+00:00",
+                "2022-10-28T11:58:50+00:00",
+            ],
+            "EndDate": [
+                "2021-12-24T14:40:27+00:00",
+                "2022-02-18T09:04:30+00:00",
+                "2022-10-28T12:35:38+00:00",
+            ],
+            "ActiveDuration": [768000, 2256000, 2208000],
+            "Calories": [89.0, 473.0, 478.0],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_diff_num_of_rows():
+    yield pd.DataFrame(
+        {
+            "LogId": ["44984262767"],
+            "StartDate": ["2021-12-24T14:27:39+00:00"],
+            "EndDate": ["2021-12-24T14:40:27+00:00"],
+            "ActiveDuration": ["768000"],
+            "Calories": ["89"],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_dup_cols():
+    dup_df = pd.DataFrame(
+        {
+            "LogId": ["44984262767"],
+            "StartDate": ["2021-12-24T14:27:39+00:00"],
+            "EndDate": ["2021-12-24T14:40:27+00:00"],
+            "ActiveDuration": [768000],
+            "Calories": [89.0],
+        }
+    )
+    dup_df = dup_df.rename({"StartDate": "EndDate"}, axis=1)
+    yield dup_df
+
+
+@pytest.fixture()
+def staging_dataset_with_dup_indexes():
+    yield pd.DataFrame(
+        {
+            "LogId": ["44984262767", "44984262767"],
+            "StartDate": ["2021-12-24T14:27:39", "2021-12-24T14:27:40"],
+            "EndDate": ["2021-12-24T14:40:27", "2021-12-24T14:40:28"],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_all_col_val_diff():
+    yield pd.DataFrame(
+        {
+            "LogId": ["44984262767", "44984262767"],
+            "StartDate": ["2021-12-24T14:27:39+00:00", "2021-12-24T14:27:39+00:00"],
+            "EndDate": ["TESTING1", "TESTING2"],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_with_empty_columns():
+    return pd.DataFrame(
+        {
+            "LogId": [],
+            "StartDate": [],
+            "EndDate": [],
+        }
+    )
+
+
+@pytest.fixture()
+def staging_dataset_empty():
+    return pd.DataFrame()
diff --git a/tests/test_compare_parquet_datasets.py b/tests/test_compare_parquet_datasets.py
new file mode 100644
index 00000000..2285e95b
--- /dev/null
+++ b/tests/test_compare_parquet_datasets.py
@@ -0,0 +1,580 @@
+import argparse
+from unittest import mock
+
+import datacompy
+from moto import mock_s3
+import pandas as pd
+from pandas.testing import assert_frame_equal
+import pytest
+from pyarrow import fs, parquet
+
+from src.glue.jobs import compare_parquet_datasets as compare_parquet
+
+
+def test_that_validate_args_raises_exception_when_input_value_is_empty_string():
+    with pytest.raises(ValueError):
+        compare_parquet.validate_args(value="")
+
+
+def test_that_validate_args_returns_nothing_when_value_is_not_an_empty_string():
+    assert compare_parquet.validate_args(value="TEST") == None
+
+
+def test_that_get_parquet_dataset_s3_path_returns_correct_filepath(parquet_bucket_name):
+    filepath = compare_parquet.get_parquet_dataset_s3_path(
+        parquet_bucket_name, "test_namespace", "dataset_fitbitactivitylogs"
+    )
+    assert (
+        filepath
+        == "s3://test-parquet-bucket/test_namespace/parquet/dataset_fitbitactivitylogs"
+    )
+
+
+def test_that_get_s3_file_key_for_comparison_results_returns_correct_filepath_for_data_types_compare():
+    file_key = compare_parquet.get_s3_file_key_for_comparison_results(
+        "staging", data_type=None, file_name="data_types_compare.txt"
+    )
+    assert file_key == "staging/comparison_result/data_types_compare.txt"
+
+
+def test_that_get_s3_file_key_for_comparison_results_has_expected_filepath_for_specific_data_type():
+    file_key = compare_parquet.get_s3_file_key_for_comparison_results(
+        "staging",
+        data_type="dataset_fitbitactivitylogs",
+        file_name="parquet_compare.txt",
+    )
+    assert (
+        file_key
+        == "staging/comparison_result/dataset_fitbitactivitylogs/parquet_compare.txt"
+    )
+
+
+def test_that_get_s3_file_key_for_comparison_results_raises_type_error_if_filename_has_wrong_file_ext():
+    with pytest.raises(TypeError):
+        file_key = compare_parquet.get_s3_file_key_for_comparison_results(
+            "staging",
+            data_type="dataset_fitbitactivitylogs",
+            file_name="parquet_compare.pdf",
+        )
+
+
+def test_that_get_duplicated_columns_returns_empty_if_no_dup_exist(
+    valid_staging_dataset,
+):
+    assert compare_parquet.get_duplicated_columns(valid_staging_dataset) == []
+
+
+def test_that_get_duplicated_columns_returns_list_if_dup_exist(
+    staging_dataset_with_dup_cols,
+):
+    assert compare_parquet.get_duplicated_columns(staging_dataset_with_dup_cols) == [
+        "EndDate"
+    ]
+
+
+@mock_s3
+def test_that_get_S3FileSystem_from_session_returns_filesystem_when_credentials_exist(
+    mock_aws_session,
+):
+    filesystem = compare_parquet.get_S3FileSystem_from_session(
+        aws_session=mock_aws_session
+    )
+    assert isinstance(filesystem, fs.S3FileSystem)
+
+
+@mock_s3
+def test_that_get_parquet_dataset_raises_attr_error_if_no_datasets_exist(
+    s3, mock_s3_filesystem, parquet_bucket_name
+):
+    file_key = "staging/parquet/dataset_fitbitactivitylogs/test.parquet"
+    with mock.patch.object(parquet, "read_table", return_value=None) as mock_method:
+        with pytest.raises(AttributeError):
+            parquet_dataset = compare_parquet.get_parquet_dataset(
+                dataset_key=f"{parquet_bucket_name}/{file_key}",
+                s3_filesystem=mock_s3_filesystem,
+            )
+
+
+@mock_s3
+def test_that_get_parquet_dataset_returns_dataset_if_datasets_exist(
+    s3,
+    mock_s3_filesystem,
+    valid_staging_parquet_object,
+    valid_staging_dataset,
+    parquet_bucket_name,
+):
+    file_key = "staging/parquet/dataset_fitbitactivitylogs/test.parquet"
+    with mock.patch.object(
+        parquet, "read_table", return_value=valid_staging_parquet_object
+    ) as mock_method:
+        parquet_dataset = compare_parquet.get_parquet_dataset(
+            dataset_key=f"{parquet_bucket_name}/{file_key}",
+            s3_filesystem=mock_s3_filesystem,
+        )
+
+        assert_frame_equal(
+            parquet_dataset.reset_index(drop=True),
+            valid_staging_dataset.reset_index(drop=True),
+        )
+
+
+@mock_s3
+def test_that_get_folders_in_s3_bucket_returns_empty_list_if_no_folders(
+    s3, parquet_bucket_name
+):
+    s3.create_bucket(Bucket=parquet_bucket_name)
+    result = compare_parquet.get_folders_in_s3_bucket(
+        s3, bucket_name=parquet_bucket_name, namespace="staging"
+    )
+    assert result == []
+
+
+@mock_s3
+def test_that_get_folders_in_s3_bucket_returns_list_if_folder_exists(
+    s3, parquet_bucket_name
+):
+    s3.create_bucket(Bucket=parquet_bucket_name)
+
+    for obj in [
+        "dataset_fitbitactivitylogs",
+        "dataset_fitbitactivitylogs/test.txt",
+        "dataset_fitbitprofiles",
+        "dataset_fitbitactivitylogs/test2.txt",
+        "dataset_fitbitprofiles/test.txt",
+    ]:
+        s3.put_object(Bucket=parquet_bucket_name, Key=f"staging/parquet/{obj}")
+
+    result = compare_parquet.get_folders_in_s3_bucket(
+        s3, bucket_name=parquet_bucket_name, namespace="staging"
+    )
+    assert result == ["dataset_fitbitactivitylogs", "dataset_fitbitprofiles"]
+
+
+def test_that_has_common_cols_returns_false_if_no_common_cols(
+    staging_dataset_with_no_common_cols, valid_main_dataset
+):
+    test_common_cols = compare_parquet.has_common_cols(
+        staging_dataset_with_no_common_cols, valid_main_dataset
+    )
+    assert test_common_cols is False
+
+
+def test_that_has_common_cols_returns_true_if_common_cols(
+    valid_staging_dataset, valid_main_dataset
+):
+    test_common_cols = compare_parquet.has_common_cols(
+        valid_staging_dataset, valid_main_dataset
+    )
+    assert test_common_cols is True
+
+
+def test_that_get_missing_cols_returns_empty_list_if_no_missing_cols(
+    valid_staging_dataset, valid_main_dataset
+):
+    test_missing_cols = compare_parquet.get_missing_cols(
+        valid_staging_dataset, valid_main_dataset
+    )
+    assert test_missing_cols == []
+
+
+def test_that_get_missing_cols_returns_list_of_cols_if_missing_cols(
+    staging_dataset_with_missing_cols, valid_main_dataset
+):
+    test_missing_cols = compare_parquet.get_missing_cols(
+        staging_dataset_with_missing_cols, valid_main_dataset
+    )
+    assert test_missing_cols == ["EndDate", "StartDate"]
+
+
+def test_that_get_additional_cols_returns_empty_list_if_no_add_cols(
+    valid_staging_dataset, valid_main_dataset
+):
+    test_add_cols = compare_parquet.get_additional_cols(
+        valid_staging_dataset, valid_main_dataset
+    )
+    assert test_add_cols == []
+
+
+def test_that_get_additional_cols_returns_list_of_cols_if_add_cols(
+    staging_dataset_with_add_cols, valid_main_dataset
+):
+    test_add_cols = compare_parquet.get_additional_cols(
+        staging_dataset_with_add_cols, valid_main_dataset
+    )
+    assert test_add_cols == ["AverageHeartRate"]
+
+
+def test_that_dataframe_to_text_returns_str(valid_staging_dataset):
+    staging_content = compare_parquet.convert_dataframe_to_text(valid_staging_dataset)
+    assert isinstance(staging_content, str)
+
+
+def test_that_dataframe_to_text_returns_valid_format_for_s3_put_object(
+    s3, parquet_bucket_name, valid_staging_dataset
+):
+    # shouldn't throw a botocore.exceptions.ParamValidationError
+    s3.create_bucket(Bucket=parquet_bucket_name)
+    staging_content = compare_parquet.convert_dataframe_to_text(valid_staging_dataset)
+    s3.put_object(
+        Bucket=parquet_bucket_name,
+        Key=f"staging/parquet/dataset_fitbitactivitylogs/test.csv",
+        Body=staging_content,
+    )
+
+
+def test_that_get_duplicates_returns_empty_df_if_no_dups(
+    valid_staging_dataset, valid_main_dataset
+):
+    compare = datacompy.Compare(
+        df1=valid_staging_dataset,
+        df2=valid_main_dataset,
+        join_columns="LogId",
+        df1_name="staging",  # Optional, defaults to 'df1'
+        df2_name="main",  # Optional, defaults to 'df2'
+        cast_column_names_lower=False,
+    )
+    staging_dups = compare_parquet.get_duplicates(compare, namespace="staging")
+    assert staging_dups.empty
+
+
+def test_that_get_duplicates_returns_dups_df_if_dups_exist(
+    staging_dataset_with_dup_indexes, valid_main_dataset
+):
+    compare = datacompy.Compare(
+        df1=staging_dataset_with_dup_indexes,
+        df2=valid_main_dataset,
+        join_columns="LogId",
+        df1_name="staging",  # Optional, defaults to 'df1'
+        df2_name="main",  # Optional, defaults to 'df2'
+        cast_column_names_lower=False,
+    )
+    staging_dups = compare_parquet.get_duplicates(compare, namespace="staging")
+    assert_frame_equal(
+        staging_dups.reset_index(drop=True),
+        pd.DataFrame(
+            {
+                "LogId": ["44984262767"],
+                "StartDate": ["2021-12-24T14:27:40"],
+                "EndDate": ["2021-12-24T14:40:28"],
+            }
+        ).reset_index(drop=True),
+    )
+
+
+def test_that_get_duplicates_raises_key_error_if_namespace_invalid():
+    with pytest.raises(KeyError):
+        compare_parquet.get_duplicates(None, namespace="invalid")
+
+
+def test_that_compare_row_diffs_returns_empty_df_if_columns_are_not_diff(
+    valid_staging_dataset, valid_main_dataset
+):
+    compare = datacompy.Compare(
+        df1=valid_staging_dataset,
+        df2=valid_main_dataset,
+        join_columns="LogId",
+        df1_name="staging",  # Optional, defaults to 'df1'
+        df2_name="main",  # Optional, defaults to 'df2'
+        cast_column_names_lower=False,
+    )
+    staging_rows = compare_parquet.compare_row_diffs(compare, namespace="staging")
+    main_rows = compare_parquet.compare_row_diffs(compare, namespace="main")
+    assert staging_rows.empty
+    assert main_rows.empty
+
+
+def test_that_compare_row_diffs_returns_df_if_columns_are_not_diff(
+    staging_dataset_with_diff_num_of_rows, valid_main_dataset
+):
+    compare = datacompy.Compare(
+        df1=staging_dataset_with_diff_num_of_rows,
+        df2=valid_main_dataset,
+        join_columns="LogId",
+        df1_name="staging",  # Optional, defaults to 'df1'
+        df2_name="main",  # Optional, defaults to 'df2'
+        cast_column_names_lower=False,
+    )
+    staging_rows = compare_parquet.compare_row_diffs(compare, namespace="staging")
+    main_rows = compare_parquet.compare_row_diffs(compare, namespace="main")
+    assert staging_rows.empty
+    assert_frame_equal(
+        main_rows.reset_index(drop=True),
+        pd.DataFrame(
+            {
+                "LogId": [
+                    "46096730542",
+                    "51739302864",
+                ],
+                "StartDate": [
+                    "2022-02-18T08:26:54+00:00",
+                    "2022-10-28T11:58:50+00:00",
+                ],
+                "EndDate": [
+                    "2022-02-18T09:04:30+00:00",
+                    "2022-10-28T12:35:38+00:00",
+                ],
+                "ActiveDuration": ["2256000", "2208000"],
+                "Calories": ["473", "478"],
+            }
+        ).reset_index(drop=True),
+    )
+
+
+def test_that_compare_row_diffs_raises_key_error_is_namespace_is_invalid():
+    with pytest.raises(KeyError):
+        compare_parquet.compare_row_diffs(None, namespace="invalid_namespace")
+
+
+def test_that_compare_column_names_returns_empty_msg_if_cols_are_same(
+    valid_staging_dataset, valid_main_dataset
+):
+    compare_msg = compare_parquet.compare_column_names(
+        "dataset_fitbitactivitylogs", valid_staging_dataset, valid_main_dataset
+    )
+    assert compare_msg == []
+
+
+def test_that_compare_column_names_returns_msg_if_cols_are_diff(
+    staging_dataset_with_no_common_cols, valid_main_dataset
+):
+    compare_msg = compare_parquet.compare_column_names(
+        "dataset_fitbitactivitylogs",
+        staging_dataset_with_no_common_cols,
+        valid_main_dataset,
+    )
+
+    assert compare_msg == [
+        "dataset_fitbitactivitylogs: Staging dataset has the following missing columns:\n"
+        "['ActiveDuration', 'Calories', 'EndDate', 'LogId', 'StartDate']",
+        "dataset_fitbitactivitylogs: Staging dataset has the following additional columns:\n"
+        "['OriginalDuration', 'ParticipantIdentifier', 'Steps']",
+    ]
+
+
+@mock_s3
+def test_that_get_data_types_to_compare_returns_correct_datatypes_in_common(
+    s3, parquet_bucket_name
+):
+    s3.create_bucket(Bucket=parquet_bucket_name)
+    for namespace in ["staging", "main"]:
+        s3.put_object(
+            Bucket=parquet_bucket_name,
+            Key=f"{namespace}/parquet/dataset_fitbitactivitylogs/test.txt",
+        )
+        s3.put_object(
+            Bucket=parquet_bucket_name,
+            Key=f"{namespace}/parquet/dataset_fitbitdevices/test.txt",
+        )
+
+    data_types = compare_parquet.get_data_types_to_compare(
+        s3, parquet_bucket_name, staging_namespace="staging", main_namespace="main"
+    )
+    assert set(data_types) == set(
+        ["dataset_fitbitdevices", "dataset_fitbitactivitylogs"]
+    )
+
+
+@mock_s3
+def test_that_get_data_types_to_compare_returns_empty_list_if_no_data_types_in_common(
+    s3, parquet_bucket_name
+):
+    s3.create_bucket(Bucket=parquet_bucket_name)
+    s3.put_object(
+        Bucket=parquet_bucket_name,
+        Key=f"staging/parquet/dataset_fitbitactivitylogs/test.txt",
+    )
+    s3.put_object(
+        Bucket=parquet_bucket_name,
+        Key=f"main/parquet/dataset_fitbitdevices/test.txt",
+    )
+
+    data_types = compare_parquet.get_data_types_to_compare(
+        s3, parquet_bucket_name, staging_namespace="staging", main_namespace="main"
+    )
+    assert data_types == []
+
+
+@mock_s3
+def test_that_compare_dataset_data_types_returns_empty_msg_if_datatypes_are_equal(
+    s3, parquet_bucket_name
+):
+    s3.create_bucket(Bucket=parquet_bucket_name)
+    for namespace in ["staging", "main"]:
+        s3.put_object(
+            Bucket=parquet_bucket_name,
+            Key=f"{namespace}/parquet/dataset_fitbitactivitylogs/test.txt",
+        )
+    compare_msg = compare_parquet.compare_dataset_data_types(
+        s3, parquet_bucket_name, staging_namespace="staging", main_namespace="main"
+    )
+    assert compare_msg == []
+
+
+@mock_s3
+def test_that_compare_dataset_data_types_returns_msg_if_datatypes_are_not_equal(
+    s3, parquet_bucket_name
+):
+    s3.create_bucket(Bucket=parquet_bucket_name)
+    for datatype in [
+        "dataset_fitbitactivitylogs/test.txt",
+        "dataset_fitbitintradaycombined/test.txt",
+    ]:
+        s3.put_object(Bucket=parquet_bucket_name, Key=f"staging/parquet/{datatype}")
+
+    for datatype in [
+        "dataset_fitbitactivitylogs/test.txt",
+        "dataset_fitbitdevices/test.txt",
+    ]:
+        s3.put_object(Bucket=parquet_bucket_name, Key=f"main/parquet/{datatype}")
+
+    compare_msg = compare_parquet.compare_dataset_data_types(
+        s3, parquet_bucket_name, staging_namespace="staging", main_namespace="main"
+    )
+    assert compare_msg == [
+        "Staging dataset has the following missing data types: ['dataset_fitbitdevices']",
+        "Staging dataset has the following additional data types: ['dataset_fitbitintradaycombined']",
+    ]
+
+
+def test_that_is_valid_dataset_returns_true_if_dataset_is_valid(valid_staging_dataset):
+    is_valid_result = compare_parquet.is_valid_dataset(valid_staging_dataset, "staging")
+    assert is_valid_result["result"]
+    assert is_valid_result["msg"] == "staging dataset has been validated."
+
+
+def test_that_is_valid_dataset_returns_false_if_dataset_is_empty(staging_dataset_empty):
+    is_valid_result = compare_parquet.is_valid_dataset(staging_dataset_empty, "staging")
+    assert is_valid_result["result"] is False
+    assert (
+        is_valid_result["msg"]
+        == "staging dataset has no data. Comparison cannot continue."
+    )
+
+
+def test_that_is_valid_dataset_returns_false_if_dataset_has_dup_cols(
+    staging_dataset_with_dup_cols,
+):
+    is_valid_result = compare_parquet.is_valid_dataset(
+        staging_dataset_with_dup_cols, "staging"
+    )
+    assert is_valid_result["result"] is False
+    assert is_valid_result["msg"] == (
+        "staging dataset has duplicated columns. Comparison cannot continue.\n"
+        "Duplicated columns:['EndDate']"
+    )
+
+
+def test_that_is_valid_dataset_returns_true_if_dataset_has_empty_cols(
+    staging_dataset_with_empty_columns,
+):
+    is_valid_result = compare_parquet.is_valid_dataset(
+        staging_dataset_with_empty_columns, "staging"
+    )
+    assert is_valid_result["result"]
+    assert is_valid_result["msg"] == "staging dataset has been validated."
+
+
+@pytest.mark.parametrize(
+    "dataset_fixture",
+    ["staging_dataset_with_empty_columns", "valid_staging_dataset"],
+    indirect=True,
+)
+def test_that_compare_datasets_and_output_report_outputs_datacompy_compare_obj_if_input_is_valid(
+    dataset_fixture, valid_main_dataset
+):
+    compare = compare_parquet.compare_datasets_and_output_report(
+        data_type="dataset_fitbitactivitylogs",
+        staging_dataset=dataset_fixture,
+        main_dataset=valid_main_dataset,
+        staging_namespace="staging",
+        main_namespace="main",
+    )
+    assert isinstance(compare, datacompy.Compare)
+    assert_frame_equal(compare.df1, dataset_fixture)
+    assert_frame_equal(compare.df2, valid_main_dataset)
+
+
+def test_that_add_additional_msg_to_comparison_report_returns_correct_updated_msg():
+    comparison_report = "some string\n\n"
+    add_msgs = ["one message", "two message"]
+    result = compare_parquet.add_additional_msg_to_comparison_report(
+        comparison_report, add_msgs, msg_type="column_name_diff"
+    )
+    assert result == (
+        "some string\n\nColumn Name Differences\n"
+        "-----------------------\n\n"
+        "one message\ntwo message"
+    )
+
+
+def test_that_add_additional_msg_to_comparison_report_throws_error_if_msg_type_not_valid():
+    comparison_report = "some string\n\n"
+    add_msgs = ["one message", "two message"]
+    with pytest.raises(ValueError):
+        result = compare_parquet.add_additional_msg_to_comparison_report(
+            comparison_report, add_msgs, msg_type="invalid_msg_type"
+        )
+
+
+def test_that_compare_datasets_by_data_type_returns_correct_msg_if_input_is_empty(
+    parquet_bucket_name, staging_dataset_empty
+):
+    with mock.patch(
+        "src.glue.jobs.compare_parquet_datasets.get_parquet_dataset",
+        return_value=staging_dataset_empty,
+    ) as mock_parquet:
+        compare_dict = compare_parquet.compare_datasets_by_data_type(
+            parquet_bucket=parquet_bucket_name,
+            staging_namespace="staging",
+            main_namespace="main",
+            s3_filesystem=None,
+            data_type="dataset_fitbitactivitylogs",
+        )
+        assert compare_dict["comparison_report"] == (
+            "\n\nParquet Dataset Comparison running for Data Type: dataset_fitbitactivitylogs\n"
+            "-----------------------------------------------------------------\n\n"
+            "staging dataset has no data. Comparison cannot continue.\n"
+            "main dataset has no data. Comparison cannot continue."
+        )
+
+
+@mock.patch("src.glue.jobs.compare_parquet_datasets.compare_datasets_and_output_report")
+def test_that_compare_datasets_by_data_type_calls_compare_datasets_by_data_type_if_input_is_valid(
+    mocked_compare_datasets, parquet_bucket_name, valid_staging_dataset
+):
+    with mock.patch(
+        "src.glue.jobs.compare_parquet_datasets.get_parquet_dataset",
+        return_value=valid_staging_dataset,
+    ) as mock_parquet:
+        compare_parquet.compare_datasets_by_data_type(
+            parquet_bucket=parquet_bucket_name,
+            staging_namespace="staging",
+            main_namespace="main",
+            s3_filesystem=None,
+            data_type="dataset_fitbitactivitylogs",
+        )
+        mocked_compare_datasets.assert_called_once()
+
+
+@mock.patch("src.glue.jobs.compare_parquet_datasets.compare_datasets_and_output_report")
+@mock.patch(
+    "src.glue.jobs.compare_parquet_datasets.has_common_cols", return_value=False
+)
+def test_that_compare_datasets_by_data_type_does_not_call_compare_datasets_by_data_type_if_input_has_no_common_cols(
+    mocked_has_common_cols,
+    mocked_compare_datasets,
+    parquet_bucket_name,
+    valid_staging_dataset,
+):
+    with mock.patch(
+        "src.glue.jobs.compare_parquet_datasets.get_parquet_dataset",
+        return_value=valid_staging_dataset,
+    ) as mock_parquet:
+        compare_parquet.compare_datasets_by_data_type(
+            parquet_bucket=parquet_bucket_name,
+            staging_namespace="staging",
+            main_namespace="main",
+            s3_filesystem=None,
+            data_type="dataset_fitbitactivitylogs",
+        )
+        mocked_compare_datasets.assert_not_called()