diff --git a/docs/reference/notebooks/amazon_review_classification_sklearn.ipynb b/docs/reference/notebooks/amazon_review_classification_sklearn.ipynb
index 836b41bf7b..d026d648a0 100644
--- a/docs/reference/notebooks/amazon_review_classification_sklearn.ipynb
+++ b/docs/reference/notebooks/amazon_review_classification_sklearn.ipynb
@@ -117,15 +117,10 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-08T20:55:58.032511Z",
-     "start_time": "2023-11-08T20:55:57.792680Z"
-    }
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Constants.\n",
     "RANDOM_SEED = 0\n",
@@ -135,8 +130,8 @@
     "TARGET_NAME = \"isHelpful\"\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/amazon_review_dataset/reviews.json\"\n",
-    "DATA_PATH = Path.home() / \".giskard\" / \"amazon_review_dataset\" / \"reviews.json\""
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/amazon_review_dataset-reviews.json.tar.gz\"\n",
+    "DATA_PATH = Path.home() / \".giskard\" / \"amazon_review_dataset\" / \"reviews.json.tar.gz\""
    ]
   },
   {
@@ -156,18 +151,12 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-08T20:56:00.293536Z",
-     "start_time": "2023-11-08T20:56:00.234306Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -181,7 +170,7 @@
     "\n",
     "def download_data(**kwargs) -> pd.DataFrame:\n",
     "    \"\"\"Download the dataset using URL.\"\"\"\n",
-    "    fetch_from_ftp(DATA_URL, DATA_PATH)\n",
+    "    fetch_demo_data(DATA_URL, DATA_PATH)\n",
     "    _df = pd.read_json(DATA_PATH, lines=True, **kwargs)\n",
     "    return _df\n",
     "\n",
@@ -215,10 +204,10 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "reviews_df = download_data()\n",
     "reviews_df = preprocess_data(reviews_df)"
diff --git a/docs/reference/notebooks/drug_classification_sklearn.ipynb b/docs/reference/notebooks/drug_classification_sklearn.ipynb
index 6c443f487b..c1d19f7d8f 100644
--- a/docs/reference/notebooks/drug_classification_sklearn.ipynb
+++ b/docs/reference/notebooks/drug_classification_sklearn.ipynb
@@ -97,17 +97,10 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "d44430add2918aa1",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2024-02-09T09:29:15.513819Z",
-     "start_time": "2024-02-09T09:29:15.470284Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Constants.\n",
     "RANDOM_SEED = 0\n",
@@ -121,9 +114,10 @@
     "NA_TO_K_CATEGORIES = ['<10', '10-20', '20-30', '>30']\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/drug_classification_dataset/drug200.csv\"\n",
-    "DATA_PATH = Path.home() / \".giskard\" / \"drug_classification_dataset\" / \"drug200.csv\""
-   ]
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/drug_classification_dataset-drug200.csv.tar.gz\"\n",
+    "DATA_PATH = Path.home() / \".giskard\" / \"drug_classification_dataset\" / \"drug200.csv.tar.gz\""
+   ],
+   "id": "a161e40415287e1f"
   },
   {
    "cell_type": "markdown",
@@ -158,7 +152,7 @@
    },
    "outputs": [],
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -172,7 +166,7 @@
     "\n",
     "def load_data() -> pd.DataFrame:\n",
     "    \"\"\"Load data.\"\"\"\n",
-    "    fetch_from_ftp(DATA_URL, DATA_PATH)\n",
+    "    fetch_demo_data(DATA_URL, DATA_PATH)\n",
     "    df = pd.read_csv(DATA_PATH)\n",
     "    return df\n",
     "\n",
diff --git a/docs/reference/notebooks/fake_real_news_classification.ipynb b/docs/reference/notebooks/fake_real_news_classification.ipynb
index cb74b26e6b..2c70e3d8cc 100644
--- a/docs/reference/notebooks/fake_real_news_classification.ipynb
+++ b/docs/reference/notebooks/fake_real_news_classification.ipynb
@@ -66,7 +66,9 @@
    "source": [
     "import os\n",
     "import string\n",
+    "import tarfile\n",
     "from pathlib import Path\n",
+    "from typing import Tuple, Callable\n",
     "from urllib.request import urlretrieve\n",
     "\n",
     "import numpy as np\n",
@@ -79,7 +81,6 @@
     "from nltk.corpus import stopwords\n",
     "from sklearn.metrics import accuracy_score\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from typing import Tuple, Callable\n",
     "\n",
     "from giskard import Dataset, Model, scan, testing"
    ]
@@ -142,7 +143,7 @@
     "RANDOM_SEED = 0\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/fake_real_news_dataset/{}\"\n",
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/fake_real_news_dataset-{}\"\n",
     "DATA_PATH = Path.home() / \".giskard\" / \"fake_real_news_dataset\""
    ]
   },
@@ -170,7 +171,7 @@
    },
    "outputs": [],
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -184,15 +185,15 @@
     "\n",
     "def fetch_dataset() -> None:\n",
     "    \"\"\"Gradually fetch all necessary files from the FTP server.\"\"\"\n",
-    "    files_to_fetch = (\"Fake.csv\", \"True.csv\", \"glove_100d.txt\")\n",
+    "    files_to_fetch = (\"Fake.csv.tar.gz\", \"True.csv.tar.gz\", \"glove_100d.txt.tar.gz\")\n",
     "    for file_name in files_to_fetch:\n",
-    "        fetch_from_ftp(DATA_URL.format(file_name), DATA_PATH / file_name)\n",
+    "        fetch_demo_data(DATA_URL.format(file_name), DATA_PATH / file_name)\n",
     "\n",
     "\n",
     "def load_data(**kwargs) -> pd.DataFrame:\n",
     "    \"\"\"Load data.\"\"\"\n",
-    "    real_df = pd.read_csv(DATA_PATH / \"True.csv\", **kwargs)\n",
-    "    fake_df = pd.read_csv(DATA_PATH / \"Fake.csv\", **kwargs)\n",
+    "    real_df = pd.read_csv(DATA_PATH / \"True.csv.tar.gz\", **kwargs)\n",
+    "    fake_df = pd.read_csv(DATA_PATH / \"Fake.csv.tar.gz\", **kwargs)\n",
     "\n",
     "    # Create target column.\n",
     "    real_df[TARGET_COLUMN_NAME] = 0\n",
@@ -380,7 +381,7 @@
     "def get_embeddings_matrix() -> np.ndarray:\n",
     "    \"\"\"Create matrix, where each row is an embedding of a specific word.\"\"\"\n",
     "    # Load glove embeddings.\n",
-    "    embeddings_dict = dict(parse_line(*line.rstrip().rsplit(' ')) for line in open(DATA_PATH / \"glove_100d.txt\"))\n",
+    "    embeddings_dict = dict(parse_line(*line.rstrip().rsplit(' ')) for line in tarfile.open(DATA_PATH / \"glove_100d.txt.tar.gz\", \"r:gz\").extractfile(\"fake_real_news_dataset-glove_100d.txt\").read().decode())\n",
     "\n",
     "    # Create embeddings matrix with glove word vectors.\n",
     "    embeddings_matrix = init_embeddings_matrix(embeddings_dict)\n",
diff --git a/docs/reference/notebooks/hotel_text_regression.ipynb b/docs/reference/notebooks/hotel_text_regression.ipynb
index 74d4538f7a..aa5cfaa90b 100644
--- a/docs/reference/notebooks/hotel_text_regression.ipynb
+++ b/docs/reference/notebooks/hotel_text_regression.ipynb
@@ -91,24 +91,18 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T12:12:05.303464Z",
-     "start_time": "2023-11-09T12:12:05.254149Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Constants.\n",
     "FEATURE_COLUMN_NAME = \"Full_Review\"\n",
     "TARGET_COLUMN_NAME = \"Reviewer_Score\"\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/hotel_text_regression_dataset/Hotel_Reviews.csv\"\n",
-    "DATA_PATH = Path.home() / \".giskard\" / \"hotel_text_regression_dataset\" / \"Hotel_Reviews.csv\""
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/hotel_text_regression_dataset-Hotel_Reviews.csv.tar.gz\"\n",
+    "DATA_PATH = Path.home() / \".giskard\" / \"hotel_text_regression_dataset\" / \"Hotel_Reviews.csv.tar.gz\""
    ]
   },
   {
@@ -142,7 +136,7 @@
    },
    "outputs": [],
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -155,7 +149,7 @@
     "\n",
     "\n",
     "def load_data(**kwargs) -> pd.DataFrame:\n",
-    "    fetch_from_ftp(DATA_URL, DATA_PATH)\n",
+    "    fetch_demo_data(DATA_URL, DATA_PATH)\n",
     "    df = pd.read_csv(DATA_PATH, **kwargs)\n",
     "\n",
     "    # Create target column.\n",
diff --git a/docs/reference/notebooks/ieee_fraud_detection_adversarial_validation.ipynb b/docs/reference/notebooks/ieee_fraud_detection_adversarial_validation.ipynb
index 3e98100999..656659ed89 100644
--- a/docs/reference/notebooks/ieee_fraud_detection_adversarial_validation.ipynb
+++ b/docs/reference/notebooks/ieee_fraud_detection_adversarial_validation.ipynb
@@ -102,23 +102,17 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T12:17:44.751420Z",
-     "start_time": "2023-11-09T12:17:44.719440Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Constants.\n",
     "TARGET_COLUMN = 'isTest'\n",
     "IDX_LABEL = 'TransactionID'\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/fraud_detection_classification_dataset/{}\"\n",
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/fraud_detection_classification_dataset-{}\"\n",
     "DATA_PATH = Path.home() / \".giskard\" / \"fraud_detection_classification_dataset\""
    ]
   },
@@ -141,18 +135,12 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T12:17:45.925766Z",
-     "start_time": "2023-11-09T12:17:45.904823Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -165,9 +153,9 @@
     "\n",
     "\n",
     "def fetch_dataset():\n",
-    "    files_to_fetch = [\"train_transaction.csv\", \"train_identity.csv\", \"test_transaction.csv\", \"test_identity.csv\"]\n",
+    "    files_to_fetch = [\"train_transaction.csv.tar.gz\", \"train_identity.csv.tar.gz\", \"test_transaction.csv.tar.gz\", \"test_identity.csv.tar.gz\"]\n",
     "    for file_name in files_to_fetch:\n",
-    "        fetch_from_ftp(DATA_URL.format(file_name), DATA_PATH / file_name)\n",
+    "        fetch_demo_data(DATA_URL.format(file_name), DATA_PATH / file_name)\n",
     "\n",
     "\n",
     "# Define data-types of transactions features.\n",
@@ -225,11 +213,11 @@
     "def read_set(_type):\n",
     "    \"\"\"Read both transactions and identity data.\"\"\"\n",
     "    print(f\"Reading transactions data...\")\n",
-    "    _df = pd.read_csv(os.path.join(DATA_PATH, f'{_type}_transaction.csv'),\n",
+    "    _df = pd.read_csv(os.path.join(DATA_PATH, f'{_type}_transaction.csv.tar.gz'),\n",
     "                      index_col=IDX_LABEL, dtype=DATA_TYPES_TRANSACTION, nrows=250)\n",
     "\n",
     "    print(f\"Reading identity data...\")\n",
-    "    _df = _df.join(pd.read_csv(os.path.join(DATA_PATH, f'{_type}_identity.csv'),\n",
+    "    _df = _df.join(pd.read_csv(os.path.join(DATA_PATH, f'{_type}_identity.csv.tar.gz'),\n",
     "                               index_col=IDX_LABEL, dtype=DATA_TYPES_ID))\n",
     "    return _df\n",
     "\n",
@@ -248,16 +236,10 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T12:17:46.316557Z",
-     "start_time": "2023-11-09T12:17:46.290804Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "def preprocess_dataset(train_set, test_set):\n",
     "    \"\"\"Unite train and test into common dataframe.\"\"\"\n",
diff --git a/docs/reference/notebooks/insurance_prediction_lgbm.ipynb b/docs/reference/notebooks/insurance_prediction_lgbm.ipynb
index 162e15d080..ad7467cd04 100644
--- a/docs/reference/notebooks/insurance_prediction_lgbm.ipynb
+++ b/docs/reference/notebooks/insurance_prediction_lgbm.ipynb
@@ -179,8 +179,8 @@
     "CATEGORICAL_COLS = [\"sex\", \"smoker\", \"region\"]\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/insurance_prediction_dataset/us_health_insurance_dataset.csv\"\n",
-    "DATA_PATH = Path.home() / \".giskard\" / \"insurance_prediction_dataset\" / \"us_health_insurance_dataset.csv\""
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/insurance_prediction_dataset-us_health_insurance_dataset.csv.tar.gz\"\n",
+    "DATA_PATH = Path.home() / \".giskard\" / \"insurance_prediction_dataset\" / \"us_health_insurance_dataset.csv.tar.gz\""
    ]
   },
   {
@@ -216,7 +216,7 @@
    },
    "outputs": [],
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -230,7 +230,7 @@
     "\n",
     "def download_data(**kwargs) -> pd.DataFrame:\n",
     "    \"\"\"Download the dataset using URL.\"\"\"\n",
-    "    fetch_from_ftp(DATA_URL, DATA_PATH)\n",
+    "    fetch_demo_data(DATA_URL, DATA_PATH)\n",
     "    _df = pd.read_csv(DATA_PATH, **kwargs)\n",
     "    return _df"
    ]
diff --git a/docs/reference/notebooks/m5_sales_prediction_lgbm.ipynb b/docs/reference/notebooks/m5_sales_prediction_lgbm.ipynb
index 06e8e45a86..cd35399500 100644
--- a/docs/reference/notebooks/m5_sales_prediction_lgbm.ipynb
+++ b/docs/reference/notebooks/m5_sales_prediction_lgbm.ipynb
@@ -74,8 +74,8 @@
     "from urllib.request import urlretrieve\n",
     "\n",
     "import pandas as pd\n",
-    "from sklearn import preprocessing\n",
     "from lightgbm import LGBMRegressor\n",
+    "from sklearn import preprocessing\n",
     "from sklearn.metrics import r2_score\n",
     "\n",
     "from giskard import Dataset, Model, scan, testing"
@@ -109,9 +109,9 @@
     "SPLIT_DATE = \"2016-03-27\"\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/m5_sales_prediction_dataset/{}\"\n",
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/m5_sales_prediction_dataset-{}\"\n",
     "DATA_PATH = Path.home() / \".giskard\" / \"m5_sales_prediction_dataset\"\n",
-    "DATA_FILES = [\"calendar.csv\", \"sales_train_validation.csv\", \"sell_prices.csv\"]"
+    "DATA_FILES = [\"calendar.csv.tar.gz\", \"sales_train_validation.csv.tar.gz\", \"sell_prices.csv.tar.gz\"]"
    ]
   },
   {
@@ -140,7 +140,7 @@
    },
    "outputs": [],
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -157,16 +157,16 @@
     "    for file_name in DATA_FILES:\n",
     "        source = DATA_URL.format(file_name)\n",
     "        destination = DATA_PATH / file_name\n",
-    "        fetch_from_ftp(source, destination)\n",
+    "        fetch_demo_data(source, destination)\n",
     "\n",
     "\n",
     "def load_data(n_series_use: int = 100) -> Tuple[pd.DataFrame, ...]:\n",
     "    \"\"\"Load necessary data files.\"\"\"\n",
     "    fetch_dataset()\n",
     "    \n",
-    "    calendar_df = pd.read_csv(DATA_PATH / \"calendar.csv\")\n",
-    "    prices_df = pd.read_csv(DATA_PATH / 'sell_prices.csv')\n",
-    "    sales_df = pd.read_csv(DATA_PATH / 'sales_train_validation.csv')\n",
+    "    calendar_df = pd.read_csv(DATA_PATH / \"calendar.csv.tar.gz\")\n",
+    "    prices_df = pd.read_csv(DATA_PATH / 'sell_prices.csv.tar.gz')\n",
+    "    sales_df = pd.read_csv(DATA_PATH / 'sales_train_validation.csv.tar.gz')\n",
     "    sales_df = sales_df.iloc[:n_series_use]\n",
     "    \n",
     "    return calendar_df, prices_df, sales_df\n",
diff --git a/docs/reference/notebooks/medical_transcript_classification_sklearn.ipynb b/docs/reference/notebooks/medical_transcript_classification_sklearn.ipynb
index 4694220c18..b94dd75911 100644
--- a/docs/reference/notebooks/medical_transcript_classification_sklearn.ipynb
+++ b/docs/reference/notebooks/medical_transcript_classification_sklearn.ipynb
@@ -95,6 +95,7 @@
    "source": [
     "import string\n",
     "from pathlib import Path\n",
+    "from typing import Iterable\n",
     "from urllib.request import urlretrieve\n",
     "\n",
     "import nltk\n",
@@ -107,7 +108,6 @@
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.pipeline import Pipeline\n",
     "from sklearn.preprocessing import FunctionTransformer\n",
-    "from typing import Iterable\n",
     "\n",
     "from giskard import Dataset, Model, scan, testing"
    ]
@@ -154,8 +154,8 @@
     "RANDOM_SEED = 8888\n",
     "\n",
     "# Data.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/medical_transcript_classification_dataset/mtsamples.csv\"\n",
-    "DATA_PATH = Path.home() / \".giskard\" / \"medical_transcript_classification_dataset\" / \"mtsamples.csv\""
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/medical_transcript_classification_dataset-mtsamples.csv.tar.gz\"\n",
+    "DATA_PATH = Path.home() / \".giskard\" / \"medical_transcript_classification_dataset\" / \"mtsamples.csv.tar.gz\""
    ]
   },
   {
@@ -214,7 +214,7 @@
    },
    "outputs": [],
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -228,7 +228,7 @@
     "\n",
     "def load_data() -> pd.DataFrame:\n",
     "    \"\"\"Load and initially preprocess data.\"\"\"\n",
-    "    fetch_from_ftp(DATA_URL, DATA_PATH)\n",
+    "    fetch_demo_data(DATA_URL, DATA_PATH)\n",
     "\n",
     "    df = pd.read_csv(DATA_PATH)\n",
     "\n",
diff --git a/docs/reference/notebooks/movie_review_sentiment_classification_pytorch_sklearn.ipynb b/docs/reference/notebooks/movie_review_sentiment_classification_pytorch_sklearn.ipynb
index 3fe599fbcd..69b110f563 100644
--- a/docs/reference/notebooks/movie_review_sentiment_classification_pytorch_sklearn.ipynb
+++ b/docs/reference/notebooks/movie_review_sentiment_classification_pytorch_sklearn.ipynb
@@ -105,8 +105,8 @@
     "RANDOM_STATE = 0\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/movie_review_sentiment_classification_dataset/train.jsonl\"\n",
-    "DATA_PATH = Path.home() / \".giskard\" / \"movie_review_sentiment_classification_dataset\" / \"train.jsonl\""
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/movie_review_sentiment_classification_dataset-train.jsonl.tar.gz\"\n",
+    "DATA_PATH = Path.home() / \".giskard\" / \"tripadvisor_reviews_dataset\" / \"train.jsonl.tar.gz\""
    ]
   },
   {
@@ -135,7 +135,7 @@
    },
    "outputs": [],
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
     "\n",
@@ -148,7 +148,7 @@
     "\n",
     "def load_data(**kwargs) -> pd.DataFrame:\n",
     "    \"\"\"Load data.\"\"\"\n",
-    "    fetch_from_ftp(DATA_URL, DATA_PATH)\n",
+    "    fetch_demo_data(DATA_URL, DATA_PATH)\n",
     "\n",
     "    df = pd.read_json(DATA_PATH, lines=True, **kwargs)\n",
     "    df = df.drop(columns=\"label_text\")\n",
diff --git a/docs/reference/notebooks/tripadvisor_sentiment_classification.ipynb b/docs/reference/notebooks/tripadvisor_sentiment_classification.ipynb
index 177c145245..3c1afd870a 100644
--- a/docs/reference/notebooks/tripadvisor_sentiment_classification.ipynb
+++ b/docs/reference/notebooks/tripadvisor_sentiment_classification.ipynb
@@ -75,6 +75,7 @@
     "import string\n",
     "from dataclasses import dataclass\n",
     "from pathlib import Path\n",
+    "from typing import Union, List\n",
     "from urllib.request import urlretrieve\n",
     "\n",
     "import nltk\n",
@@ -85,7 +86,6 @@
     "from torch.utils.data import DataLoader\n",
     "from torch.utils.data import TensorDataset\n",
     "from transformers import DistilBertForSequenceClassification, DistilBertTokenizer\n",
-    "from typing import Union, List\n",
     "\n",
     "from giskard import Dataset, Model, scan, testing"
    ]
@@ -123,9 +123,9 @@
     "STOP_WORDS = set(stopwords.words('english'))\n",
     "RANDOM_SEED = 0\n",
     "\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/tripadvisor_reviews_dataset/{}\"\n",
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/tripadvisor_reviews_dataset-{}\"\n",
     "DATA_PATH = Path.home() / \".giskard\" / \"tripadvisor_reviews_dataset\"\n",
-    "DATA_FILE_NAME = \"tripadvisor_hotel_reviews.csv\""
+    "DATA_FILE_NAME = \"tripadvisor_hotel_reviews.csv.tar.gz\""
    ]
   },
   {
@@ -181,7 +181,7 @@
     "\n",
     "\n",
     "# Define data download and pre-processing functions\n",
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
     "\n",
@@ -277,7 +277,7 @@
     "\n",
     "def load_dataset() -> pd.DataFrame:\n",
     "    # Download dataset\n",
-    "    fetch_from_ftp(DATA_URL.format(DATA_FILE_NAME), DATA_PATH / DATA_FILE_NAME)\n",
+    "    fetch_demo_data(DATA_URL.format(DATA_FILE_NAME), DATA_PATH / DATA_FILE_NAME)\n",
     "    df = pd.read_csv(DATA_PATH / DATA_FILE_NAME, nrows=MAX_NUM_ROWS)\n",
     "    # Obtain labels for our task.\n",
     "    df[TARGET_COLUMN_NAME] = df.Rating.apply(lambda x: create_label(x))\n",
diff --git a/docs/reference/notebooks/wage_classification.ipynb b/docs/reference/notebooks/wage_classification.ipynb
index d1053a4b7e..1cb2c755eb 100644
--- a/docs/reference/notebooks/wage_classification.ipynb
+++ b/docs/reference/notebooks/wage_classification.ipynb
@@ -90,16 +90,10 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T16:41:07.873921Z",
-     "start_time": "2023-11-09T16:41:07.824526Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Constants\n",
     "RANDOM_SEED = 0\n",
@@ -131,8 +125,8 @@
     "TARGET_COLUMN = \"income\"\n",
     "\n",
     "# Paths.\n",
-    "DATA_URL = \"ftp://sys.giskard.ai/pub/unit_test_resources/wage_classification_dataset/adult.csv\"\n",
-    "DATA_PATH = Path.home() / \".giskard\" / \"wage_classification_dataset\" / \"adult.csv\""
+    "DATA_URL = \"https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/wage_classification_dataset-adult.csv.tar.gz\"\n",
+    "DATA_PATH = Path.home() / \".giskard\" / \"wage_classification_dataset\" / \"adult.csv.tar.gz\""
    ]
   },
   {
@@ -154,18 +148,12 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-11-09T16:41:09.554619Z",
-     "start_time": "2023-11-09T16:41:09.531213Z"
-    },
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
-    "def fetch_from_ftp(url: str, file: Path) -> None:\n",
+    "def fetch_demo_data(url: str, file: Path) -> None:\n",
     "    \"\"\"Helper to fetch data from the FTP server.\"\"\"\n",
     "    if not file.parent.exists():\n",
     "        file.parent.mkdir(parents=True, exist_ok=True)\n",
@@ -179,7 +167,7 @@
     "\n",
     "def download_data(**kwargs) -> pd.DataFrame:\n",
     "    \"\"\"Download the dataset using URL.\"\"\"\n",
-    "    fetch_from_ftp(DATA_URL, DATA_PATH)\n",
+    "    fetch_demo_data(DATA_URL, DATA_PATH)\n",
     "    _df = pd.read_csv(DATA_PATH, **kwargs)\n",
     "    return _df\n",
     "\n",
@@ -192,12 +180,10 @@
    ]
   },
   {
+   "metadata": {},
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
    "outputs": [],
+   "execution_count": null,
    "source": [
     "income_df = download_data()\n",
     "income_df = preprocess_data(income_df)"
diff --git a/tests/fixtures/amazon_review__binary_classification.py b/tests/fixtures/amazon_review__binary_classification.py
index f19d883a09..0550bece7b 100644
--- a/tests/fixtures/amazon_review__binary_classification.py
+++ b/tests/fixtures/amazon_review__binary_classification.py
@@ -12,7 +12,7 @@
 
 from giskard import Dataset
 from giskard.models.sklearn import SKLearnModel
-from tests.url_utils import fetch_from_ftp
+from tests.url_utils import fetch_test_data
 
 # Constants.
 RANDOM_SEED = 0
@@ -24,12 +24,12 @@
 FEATURE_COLUMN_NAME = "reviewText"
 
 # Data.
-DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/amazon_review_dataset/reviews.json"
-DATA_PATH = Path.home() / ".giskard" / "amazon_review_dataset" / "reviews.json"
+DATA_URL = "https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/amazon_review_dataset-reviews.json.tar.gz"
+DATA_PATH = Path.home() / ".giskard" / "amazon_review_dataset" / "reviews.json.tar.gz"
 
 
 def download_data(**kwargs) -> pd.DataFrame:
-    fetch_from_ftp(DATA_URL, DATA_PATH)
+    fetch_test_data(DATA_URL, DATA_PATH)
     _df = pd.read_json(DATA_PATH, lines=True, **kwargs)
     return _df
 
diff --git a/tests/fixtures/drug_classification__multiclass_classification.py b/tests/fixtures/drug_classification__multiclass_classification.py
index 4366edf300..1132227cf3 100644
--- a/tests/fixtures/drug_classification__multiclass_classification.py
+++ b/tests/fixtures/drug_classification__multiclass_classification.py
@@ -11,11 +11,13 @@
 
 from giskard import Dataset
 from giskard.models.sklearn import SKLearnModel
-from tests.url_utils import fetch_from_ftp
+from tests.url_utils import fetch_test_data
 
 # Data.
-DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/drug_classification_dataset/drug200.csv"
-DATA_PATH = Path.home() / ".giskard" / "drug_classification_dataset" / "drug200.csv"
+DATA_URL = (
+    "https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/drug_classification_dataset-drug200.csv.tar.gz"
+)
+DATA_PATH = Path.home() / ".giskard" / "drug_classification_dataset" / "drug200.csv.tar.gz"
 
 # Constants.
 TARGET_NAME = "Drug"
@@ -50,7 +52,7 @@ def _bin_na_to_k(_df: pd.DataFrame) -> pd.DataFrame:
 @pytest.fixture(scope="session")
 def drug_classification_raw_data() -> pd.DataFrame:
     # Download data.
-    fetch_from_ftp(DATA_URL, DATA_PATH)
+    fetch_test_data(DATA_URL, DATA_PATH)
 
     # Load and wrap data.
     raw_data = bin_numerical(pd.read_csv(DATA_PATH))
diff --git a/tests/fixtures/fraud_detection__binary_classification.py b/tests/fixtures/fraud_detection__binary_classification.py
index f4c64af4a3..d52d3ff7c3 100644
--- a/tests/fixtures/fraud_detection__binary_classification.py
+++ b/tests/fixtures/fraud_detection__binary_classification.py
@@ -9,10 +9,10 @@
 from sklearn.model_selection import train_test_split
 
 from giskard import Dataset, Model
-from tests.url_utils import fetch_from_ftp
+from tests.url_utils import fetch_test_data
 
 # Data.
-DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/fraud_detection_classification_dataset/{}"
+DATA_URL = "https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/fraud_detection_classification_dataset-{}"
 DATA_PATH = Path.home() / ".giskard" / "fraud_detection_classification_dataset"
 
 # Constants.
@@ -86,9 +86,14 @@
 
 
 def fetch_dataset():
-    files_to_fetch = ["train_transaction.csv", "train_identity.csv", "test_transaction.csv", "test_identity.csv"]
+    files_to_fetch = [
+        "train_transaction.csv.tar.gz",
+        "train_identity.csv.tar.gz",
+        "test_transaction.csv.tar.gz",
+        "test_identity.csv.tar.gz",
+    ]
     for file_name in files_to_fetch:
-        fetch_from_ftp(DATA_URL.format(file_name), DATA_PATH / file_name)
+        fetch_test_data(DATA_URL.format(file_name), DATA_PATH / file_name)
 
 
 def read_set(_type, nrows=150):
@@ -96,9 +101,9 @@ def read_set(_type, nrows=150):
     fetch_dataset()
 
     _df = pd.read_csv(
-        DATA_PATH / f"{_type}_transaction.csv", index_col=IDX_LABEL, dtype=DATA_TYPES_TRANSACTION, nrows=nrows
+        DATA_PATH / f"{_type}_transaction.csv.tar.gz", index_col=IDX_LABEL, dtype=DATA_TYPES_TRANSACTION, nrows=nrows
     )
-    _df = _df.join(pd.read_csv(DATA_PATH / f"{_type}_identity.csv", index_col=IDX_LABEL, dtype=DATA_TYPES_ID))
+    _df = _df.join(pd.read_csv(DATA_PATH / f"{_type}_identity.csv.tar.gz", index_col=IDX_LABEL, dtype=DATA_TYPES_ID))
 
     return _df
 
diff --git a/tests/fixtures/hotel_text__regression.py b/tests/fixtures/hotel_text__regression.py
index 32a0d891cf..ba9ba21081 100644
--- a/tests/fixtures/hotel_text__regression.py
+++ b/tests/fixtures/hotel_text__regression.py
@@ -11,11 +11,11 @@
 
 from giskard import Dataset
 from giskard.models.sklearn import SKLearnModel
-from tests.url_utils import fetch_from_ftp
+from tests.url_utils import fetch_test_data
 
 # Data.
-DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/hotel_text_regression_dataset/Hotel_Reviews.csv"
-DATA_PATH = Path.home() / ".giskard" / "hotel_text_regression_dataset" / "Hotel_Reviews.csv"
+DATA_URL = "https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/hotel_text_regression_dataset-Hotel_Reviews.csv.tar.gz"
+DATA_PATH = Path.home() / ".giskard" / "hotel_text_regression_dataset" / "Hotel_Reviews.csv.tar.gz"
 
 # Constants.
 FEATURE_COLUMN_NAME = "Full_Review"
@@ -33,7 +33,7 @@ def load_data(**kwargs) -> pd.DataFrame:
 
 @pytest.fixture(scope="session")
 def hotel_text_raw_data():
-    fetch_from_ftp(DATA_URL, DATA_PATH)
+    fetch_test_data(DATA_URL, DATA_PATH)
 
     raw_data = load_data(nrows=105)[[FEATURE_COLUMN_NAME, TARGET_COLUMN_NAME]]
     return raw_data
diff --git a/tests/fixtures/medical_transcript_multiclass_classification.py b/tests/fixtures/medical_transcript_multiclass_classification.py
index ea4acf349d..8652a75227 100644
--- a/tests/fixtures/medical_transcript_multiclass_classification.py
+++ b/tests/fixtures/medical_transcript_multiclass_classification.py
@@ -12,7 +12,7 @@
 
 from giskard import Dataset
 from giskard.models.sklearn import SKLearnModel
-from tests.url_utils import fetch_from_ftp
+from tests.url_utils import fetch_test_data
 
 # Constants.
 LABELS_LIST = [
@@ -29,13 +29,13 @@
 LANGUAGE = "english"
 
 # Paths.
-DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/medical_transcript_classification_dataset/mtsamples.csv"
-DATA_PATH = Path.home() / ".giskard" / "medical_transcript_classification_dataset" / "mtsamples.csv"
+DATA_URL = "https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/medical_transcript_classification_dataset-mtsamples.csv.tar.gz"
+DATA_PATH = Path.home() / ".giskard" / "medical_transcript_classification_dataset" / "mtsamples.csv.tar.gz"
 
 
 def load_data() -> pd.DataFrame:
     # Download dataset.
-    fetch_from_ftp(DATA_URL, DATA_PATH)
+    fetch_test_data(DATA_URL, DATA_PATH)
     df = pd.read_csv(DATA_PATH)
 
     # Drop useless columns.
diff --git a/tests/fixtures/tripadvisor_text_classification_torch.py b/tests/fixtures/tripadvisor_text_classification_torch.py
index 78002c551b..61977a5b48 100644
--- a/tests/fixtures/tripadvisor_text_classification_torch.py
+++ b/tests/fixtures/tripadvisor_text_classification_torch.py
@@ -13,12 +13,12 @@
 from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
 
 from giskard import Dataset, Model, models
-from tests.url_utils import fetch_from_ftp
+from tests.url_utils import fetch_test_data
 
 # Data
-DATA_URL = "ftp://sys.giskard.ai/pub/unit_test_resources/tripadvisor_reviews_dataset/{}"
+DATA_URL = "https://giskard-library-test-datasets.s3.eu-north-1.amazonaws.com/tripadvisor_reviews_dataset-{}"
 DATA_PATH = Path.home() / ".giskard" / "tripadvisor_reviews_dataset"
-DATA_FILE_NAME = "tripadvisor_hotel_reviews.csv"
+DATA_FILE_NAME = "tripadvisor_hotel_reviews.csv.tar.gz"
 
 # Constants
 PRETRAINED_WEIGHTS_NAME = "distilbert-base-uncased"
@@ -115,7 +115,7 @@ def text_preprocessor(df: pd.DataFrame) -> pd.DataFrame:
 
 def load_dataset() -> pd.DataFrame:
     # Download dataset
-    fetch_from_ftp(DATA_URL.format(DATA_FILE_NAME), DATA_PATH / DATA_FILE_NAME)
+    fetch_test_data(DATA_URL.format(DATA_FILE_NAME), DATA_PATH / DATA_FILE_NAME)
     df = pd.read_csv(DATA_PATH / DATA_FILE_NAME, nrows=MAX_NUM_ROWS)
     # Obtain labels for our task.
     df[TARGET_COLUMN_NAME] = df.Rating.apply(lambda x: create_label(x))
diff --git a/tests/url_utils.py b/tests/url_utils.py
index e198afd87d..074cb0dac6 100644
--- a/tests/url_utils.py
+++ b/tests/url_utils.py
@@ -2,7 +2,7 @@
 from urllib.request import urlretrieve
 
 
-def fetch_from_ftp(url: str, file: Path) -> None:
+def fetch_test_data(url: str, file: Path) -> None:
     if not file.parent.exists():
         file.parent.mkdir(parents=True, exist_ok=True)