diff --git a/apt/utils/datasets/__init__.py b/apt/utils/datasets/__init__.py
new file mode 100644
index 0000000..d80fd72
--- /dev/null
+++ b/apt/utils/datasets/__init__.py
@@ -0,0 +1,6 @@
+"""
+The AI Privacy Toolbox (datasets).
+Implementation of datasets utility components for datasets creation, load, and store
+"""
+
+from apt.utils.datasets.datasets import DatasetABC, StoredDatasetABC, DatasetFactory, Data, BaseDataset
diff --git a/apt/utils/datasets/datasets.py b/apt/utils/datasets/datasets.py
new file mode 100644
index 0000000..c9fa0a5
--- /dev/null
+++ b/apt/utils/datasets/datasets.py
@@ -0,0 +1,226 @@
+# !/usr/bin/env python
+"""
+The AI Privacy Toolbox (datasets).
+Implementation of utility classes for dataset handling
+"""
+
+from abc import ABCMeta, abstractmethod
+from typing import Callable, Collection, Any
+
+import tarfile
+import os
+import urllib.request
+import numpy as np
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+class DatasetABC(metaclass=ABCMeta):
+    """Base Abstract Class for Dataset"""
+
+    @abstractmethod
+    def __init__(self, **kwargs):
+        pass
+
+    @abstractmethod
+    def get_samples(self) -> Collection[Any]:
+        """Return data samples"""
+        pass
+
+    @abstractmethod
+    def get_labels(self) -> Collection[Any]:
+        """Return labels"""
+        pass
+
+
+class StoredDatasetABC(DatasetABC):
+    """Abstract Class for Storable Dataset"""
+
+    @abstractmethod
+    def load_from_file(self, path: str):
+        """Load dataset from file"""
+        pass
+
+    @abstractmethod
+    def load(self, **kwargs):
+        """Load dataset"""
+        pass
+
+    @staticmethod
+    def download(url: str, dest_path: str, filename: str, unzip: bool = False) -> None:
+        """
+        Download the dataset from URL
+        :param url: dataset URL, the dataset will be requested from this URL
+        :param dest_path: local dataset destination path
+        :param filename: local dataset filename
+        :param unzip: flag whether or not perform extraction
+        :return: None
+        """
+        file_path = os.path.join(dest_path, filename)
+
+        if os.path.exists(file_path):
+            logger.warning("Files already downloaded, skipping downloading")
+
+        else:
+            os.makedirs(dest_path, exist_ok=True)
+            logger.info("Downloading the dataset...")
+            urllib.request.urlretrieve(url, file_path)
+            logger.info('Dataset Downloaded')
+
+        if unzip:
+            StoredDatasetABC.extract_archive(zip_path=file_path, dest_path=dest_path, remove_archive=False)
+
+
+    @staticmethod
+    def extract_archive(zip_path: str, dest_path=None, remove_archive=False):
+        """
+        Extract dataset from archived file
+        :param zip_path: path to archived file
+        :param dest_path: directory path to uncompress the file to
+        :param remove_archive: whether remove the archive file after uncompress (default False)
+        :return: None
+        """
+        logger.info("Extracting the dataset...")
+        tar = tarfile.open(zip_path)
+        tar.extractall(path=dest_path)
+
+        logger.info("Dataset was extracted to {}".format(dest_path))
+        if remove_archive:
+            logger.info("Removing a zip file")
+            os.remove(zip_path)
+        logger.info("Extracted the dataset")
+
+    @staticmethod
+    def split_debug(datafile: str, dest_datafile: str, ratio: int, shuffle=True, delimiter=",", fmt=None) -> None:
+        """
+        Split the data and take only a part of it
+        :param datafile: dataset file path
+        :param dest_datafile: destination path for the partial dataset file
+        :param ratio: part of the dataset to save
+        :param shuffle: whether to shuffle the data or not (default True)
+        :param delimiter: dataset delimiter (default ",")
+        :param fmt: format for the correct data saving
+        :return: None
+        """
+        if os.path.isfile(dest_datafile):
+            logger.info(f"The partial debug split already exists {dest_datafile}")
+            return
+        else:
+            os.makedirs(os.path.dirname(dest_datafile), exist_ok=True)
+
+        data = np.genfromtxt(datafile, delimiter=delimiter)
+        if shuffle:
+            logger.info("Shuffling data")
+            np.random.shuffle(data)
+
+        debug_data = data[:int(len(data) * ratio)]
+        logger.info(f"Saving {ratio} of the data to {dest_datafile}")
+        np.savetxt(dest_datafile, debug_data, delimiter=delimiter, fmt=fmt)
+
+
+class BaseDataset(DatasetABC):
+    """Base Class for Dataset"""
+
+    def __init__(self, x, y, **kwargs):
+        """
+        BaseDataset constructor.
+        :param x: collection of data samples
+        :param y: collection of labels
+        :param kwargs: dataset parameters
+        """
+        self.x = x
+        self.y = y
+
+        if len(self.x) != len(self.y):
+            raise ValueError('Non equivalent lengths of x and y')
+
+    def get_samples(self) -> Collection[Any]:
+        """Return data samples"""
+        return self.x
+
+    def get_labels(self) -> Collection[Any]:
+        """Return labels"""
+        return self.y
+
+
+class DatasetFactory:
+    """Factory class for dataset creation"""
+    registry = {}
+
+    @classmethod
+    def register(cls, name: str) -> Callable:
+        """
+        Class method to register Dataset to the internal registry
+        :param name: dataset name
+        :return:
+        """
+        def inner_wrapper(wrapped_class: DatasetABC) -> Any:
+            if name in cls.registry:
+                logger.warning('Dataset %s already exists. Will replace it', name)
+            cls.registry[name] = wrapped_class
+            return wrapped_class
+
+        return inner_wrapper
+
+    @classmethod
+    def create_dataset(cls, name: str, **kwargs) -> DatasetABC:
+        """
+        Factory command to create dataset instance.
+        This method gets the appropriate Dataset class from the registry
+        and creates an instance of it, while passing in the parameters
+        given in ``kwargs``.
+        :param name: The name of the dataset to create.
+        :param kwargs: dataset parameters
+        :return: An instance of the dataset that is created.
+        """
+        if name not in cls.registry:
+            msg = f'Dataset {name} does not exist in the registry'
+            logger.error(msg)
+            raise ValueError(msg)
+
+        exec_class = cls.registry[name]
+        executor = exec_class(**kwargs)
+        return executor
+
+
+class Data:
+
+    def __init__(self, train: DatasetABC = None, test: DatasetABC = None, **kwargs):
+        """
+        Data class constructor.
+        The class stores train and test datasets.
+        If neither of the datasets was provided,
+        Both train and test datasets will be create using
+        Factory command to create dataset instance
+        """
+        if train or test:
+            self.train = train
+            self.test = test
+        else:
+            self.train = DatasetFactory.create_dataset(train=True, **kwargs)
+            self.test = DatasetFactory.create_dataset(train=False, **kwargs)
+
+    def get_train_set(self) -> DatasetABC:
+        """Return train DatasetBase"""
+        return self.train
+
+    def get_test_set(self) -> DatasetABC:
+        """Return test DatasetBase"""
+        return self.test
+
+    def get_train_samples(self):
+        """Return train set samples"""
+        return self.train.get_samples()
+
+    def get_train_labels(self):
+        """Return train set labels"""
+        return self.train.get_labels()
+
+    def get_test_samples(self):
+        """Return test set samples"""
+        return self.test.get_samples()
+
+    def get_test_labels(self):
+        """Return test set labels"""
+        return self.test.get_labels()
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