diff --git a/modules/ml-pipeline/src/pipeline/src/build_model.py b/modules/ml-pipeline/src/pipeline/src/build_model.py
index dde3035..029ad99 100644
--- a/modules/ml-pipeline/src/pipeline/src/build_model.py
+++ b/modules/ml-pipeline/src/pipeline/src/build_model.py
@@ -12,7 +12,6 @@ from core.Logger import logger
 from core.interface.InterfaceModels import MLModel
 from core.interface.InterfaceDataClient import DataClient
 from core.DataClient import dataclient_factory
-from core.DataHandler import datahandler_factory
 from core.MLModels import model_factory
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
@@ -46,14 +45,12 @@ def build_model(
     if train_data is None:
         if train_filepath is None:
             raise ValueError(f"Need {train_filepath} if no data supplied")
-        train_data = datahandler.load_data(
-            dataclient=dataclient, location=train_filepath
-        )
+        train_data = dataclient.load_data(location=train_filepath)
 
     if test_data is None:
         if test_filepath is None:
             raise ValueError(f"Need {test_filepath} if no data supplied")
-        test_data = datahandler.load_data(dataclient=dataclient, location=test_filepath)
+        test_data = dataclient.load_data(location=test_filepath)
 
     logger.info("----------------------")
     logger.info("--- Training model ---")
@@ -80,14 +77,9 @@ if __name__ == "__main__":
     logger.info(f"--- Initiate DataClient ---")
     logger.info("----------------------------")
 
+    # Output of previous prepare data step, will be where the data is
     dataclient = dataclient_factory(prepare_data_params["output_dataclient_type"])
 
-    logger.info("-----------------------------")
-    logger.info(f"--- Initiate DataHandler ---")
-    logger.info("-----------------------------")
-
-    datahandler = datahandler_factory(prepare_data_params["datahandler_type"])
-
     logger.info("-------------------------")
     logger.info(f"--- Initiate MLModel ---")
     logger.info("-------------------------")
diff --git a/modules/ml-pipeline/src/pipeline/src/core/DataClient.py b/modules/ml-pipeline/src/pipeline/src/core/DataClient.py
index 98ad821..c8c9f2c 100644
--- a/modules/ml-pipeline/src/pipeline/src/core/DataClient.py
+++ b/modules/ml-pipeline/src/pipeline/src/core/DataClient.py
@@ -3,6 +3,7 @@ Implementations of the DataClient Protocol
 """
 
 import os
+import json
 import boto3
 import pandas as pd
 from pathlib import Path
@@ -13,7 +14,7 @@ from core.Logger import logger
 
 
 def dataclient_factory(
-    dataclient_type: str, dataclient_config: Union[dict, None]
+    dataclient_type: str, dataclient_config: Union[dict, None] = None
 ) -> DataClient:
     """
     Determine which dataclient to use
@@ -77,7 +78,7 @@ class AWSS3Client:
             )
 
     def load_data(
-        self, location: str, filetype: str, load_config: Union[dict, None] = None
+        self, location: str, load_config: Union[dict, None] = None
     ) -> pd.DataFrame:
         """
         Generic to load data
@@ -89,6 +90,8 @@ class AWSS3Client:
         if load_config is None:
             load_config = {}
 
+        filetype = Path(location).suffix
+
         load_methods = {
             ".parquet": self._load_parquet,
             # "": _load_directory(**load_config),
@@ -104,7 +107,6 @@ class AWSS3Client:
         self,
         obj: object,
         location: str,
-        filetype: str,
         save_config: Union[dict, None] = None,
     ) -> None:
         """
@@ -117,6 +119,8 @@ class AWSS3Client:
         if save_config is None:
             save_config = {}
 
+        filetype = Path(location).suffix
+
         save_methods = {
             ".parquet": self._save_parquet,
             # "": _save_directory(**save_config),
@@ -196,7 +200,7 @@ class LocalClient:
         logger.info("Local - No establishing client required")
 
     def load_data(
-        self, location: str, filetype: str, load_config: Union[dict, None] = None
+        self, location: str, load_config: Union[dict, None] = None
     ) -> pd.DataFrame:
         """
         Generic to load data
@@ -205,6 +209,8 @@ class LocalClient:
         if load_config is None:
             load_config = {}
 
+        filetype = Path(location).suffix
+
         load_methods = {
             ".parquet": self._load_parquet,
             # "": _load_directory(**load_config),
@@ -220,7 +226,6 @@ class LocalClient:
         self,
         obj: object,
         location: str,
-        filetype: str,
         save_config: Union[dict, None] = None,
     ) -> None:
         """
@@ -234,10 +239,13 @@ class LocalClient:
 
         save_methods = {
             ".parquet": self._save_parquet,
+            ".json": self._save_json
             # "": _save_directory(**save_config),
             # ADD MORE save_methods HERE
         }
 
+        filetype = Path(location).suffix
+
         if filetype not in save_methods:
             raise ValueError("save_methods specified is not in factory")
 
@@ -254,31 +262,29 @@ class LocalClient:
 
         return df
 
-    def _save_parquet(self, obj: object, location: str, save_config: dict):
+    def _save_parquet(self, obj: pd.DataFrame, location: str, save_config: dict):
         """
         Save object as parquet
         """
 
         obj.to_parquet(location, **save_config)
 
-    # def load_data_as_buffer(self, location: str) -> BytesIO:
-    #     """
-    #     When the client is established, we can load data from a buffer
-    #     """
-    #     with open(location, "rb") as file:
-    #         # Read the entire file into a BytesIO object
-    #         buffer = BytesIO(file.read())
-    #     buffer.seek(0)
+    def _save_json(self, obj: dict, location: str, save_config: dict):
+        """
+        Save object as json
+        """
+        # Serialize the dictionary to a JSON-formatted string
+        json_string = json.dumps(obj)  # indent for pretty formatting
 
-    #     return buffer
+        # Convert the JSON string to bytes (UTF-8 encoding)
+        json_bytes = json_string.encode("utf-8")
 
-    # def upload_data_from_buffer(self, buffer: BytesIO, location: str) -> None:
-    #     """
-    #     When the client is established, we can save out objects from a buffer
-    #     """
-    #     if not Path(location).parent.exists():
-    #         os.makedirs(Path(location).parent)
+        # Create a BytesIO object and write the JSON bytes to it
+        buffer = BytesIO()
+        buffer.write(json_bytes)
 
-    #     # Write the contents of the buffer to the local file
-    #     with open(location, "wb") as f:
-    #         f.write(buffer.getvalue())
+        buffer.seek(0)
+
+        # Write the contents of the buffer to the local file
+        with open(location, "wb") as f:
+            f.write(buffer.getvalue())
diff --git a/modules/ml-pipeline/src/pipeline/src/core/DataHandler.py b/modules/ml-pipeline/src/pipeline/src/core/DataHandler.py
deleted file mode 100644
index f5c07c1..0000000
--- a/modules/ml-pipeline/src/pipeline/src/core/DataHandler.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""
-Implementations of the datahandler Protocol
-"""
-
-import json
-import pandas as pd
-from io import BytesIO
-from typing import List
-from core.interface.InterfaceDataHandler import DataHandler
-from core.interface.InterfaceDataClient import DataClient
-
-
-def datahandler_factory(datahandler_type: str) -> DataHandler:
-    """
-    Determine which dataclient to use
-    """
-    datahandler = {
-        "parquet": ParquetHandler(),
-        "json": JSONHandler()
-        # ADD MORE DATACLIENTS HERE
-    }
-
-    if datahandler_type not in datahandler:
-        raise ValueError("Dataloader type specified is not in factory")
-
-    return datahandler[datahandler_type]
-
-
-def validate_dict_keys(keys_1: List[str], keys_2: List[str], config_type: str):
-    if not set(keys_1).issubset(keys_2):
-        raise ValueError(f"Incorrect {config_type} keys specified")
-
-
-class ParquetHandler:
-    """
-    Load and save Parquet datasets
-    """
-
-    def load_data(self, dataclient: DataClient, location: str) -> pd.DataFrame:
-        """
-        When the client is established, we can load data
-        """
-        df = pd.read_parquet(dataclient.load_data_as_buffer(location=location))
-        return df
-
-    def save_data(
-        self, dataclient: DataClient, obj: pd.DataFrame, location: str
-    ) -> None:
-        """
-        When the client is established, we can save out objects
-        """
-        # Convert the Pandas DataFrame to a Parquet buffer
-        parquet_buffer = BytesIO()
-        obj.to_parquet(parquet_buffer, index=False)
-
-        dataclient.upload_data_from_buffer(buffer=parquet_buffer, location=location)
-
-
-class JSONHandler:
-    """
-    Load and save Parquet datasets
-    """
-
-    def load_data(self, dataclient: DataClient, location: str) -> pd.DataFrame:
-        """
-        When the client is established, we can load data
-        """
-        ...
-
-    def save_data(self, dataclient: DataClient, obj: dict, location: str) -> None:
-        """
-        When the client is established, we can save out objects
-        """
-        # Serialize the dictionary to a JSON-formatted string
-        json_string = json.dumps(obj)  # indent for pretty formatting
-
-        # Convert the JSON string to bytes (UTF-8 encoding)
-        json_bytes = json_string.encode("utf-8")
-
-        # Create a BytesIO object and write the JSON bytes to it
-        buffer = BytesIO()
-        buffer.write(json_bytes)
-
-        buffer.seek(0)
-
-        dataclient.upload_data_from_buffer(buffer=buffer, location=location)
diff --git a/modules/ml-pipeline/src/pipeline/src/core/interface/InterfaceDataClient.py b/modules/ml-pipeline/src/pipeline/src/core/interface/InterfaceDataClient.py
index bfd8687..d572c2b 100644
--- a/modules/ml-pipeline/src/pipeline/src/core/interface/InterfaceDataClient.py
+++ b/modules/ml-pipeline/src/pipeline/src/core/interface/InterfaceDataClient.py
@@ -18,15 +18,13 @@ class DataClient(Protocol):
         """
         ...
 
-    def load_data(
-        self, location: str, filetype: str, load_config: Union[dict, None]
-    ) -> pd.DataFrame:
+    def load_data(self, location: str, load_config: Union[dict, None]) -> pd.DataFrame:
         """
         Generic to load data
         """
 
     def save_data(
-        self, obj: object, location: str, filetype: str, save_config: Union[dict, None]
+        self, obj: object, location: str, save_config: Union[dict, None]
     ) -> None:
         """
         Generic to save data
diff --git a/modules/ml-pipeline/src/pipeline/src/core/interface/InterfaceDataHandler.py b/modules/ml-pipeline/src/pipeline/src/core/interface/InterfaceDataHandler.py
deleted file mode 100644
index 1c21144..0000000
--- a/modules/ml-pipeline/src/pipeline/src/core/interface/InterfaceDataHandler.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
-Interface for all DataHandler i.e. Parquet data, csv data
-"""
-
-import pandas as pd
-from typing import Protocol, Union, Any
-from core.interface.InterfaceDataClient import DataClient
-
-
-class DataHandler(Protocol):
-    """
-    Declare the methods required for a DataClient
-    """
-
-    def load_data(self, dataclient: DataClient, location: str) -> pd.DataFrame:
-        """
-        When the client is established, we can load data
-        """
-        ...
-
-    def save_data(
-        self, dataclient: DataClient, obj: Union[pd.DataFrame, dict, Any], location: str
-    ) -> None:
-        """
-        When the client is established, we can save out objects
-        """
diff --git a/modules/ml-pipeline/src/pipeline/src/dvc.lock b/modules/ml-pipeline/src/pipeline/src/dvc.lock
index 33000e8..1f805aa 100644
--- a/modules/ml-pipeline/src/pipeline/src/dvc.lock
+++ b/modules/ml-pipeline/src/pipeline/src/dvc.lock
@@ -5,8 +5,8 @@ stages:
     deps:
     - path: prepare_data.py
       hash: md5
-      md5: 9c31bfb1b75ea3c9685ec459cbb50e62
-      size: 5921
+      md5: 2cfe9e3012280e0cecdb84da12c974d9
+      size: 5009
     params:
       configs/prepare_data.yaml:
         output_test_filepath: ./data/prepared_data/test.parquet
@@ -15,20 +15,20 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: 5cbabd20ff23b9d6734c5c68684dc8dc.dir
-      size: 11982694
+      md5: ea0a2baf3931e692d6344ba609331089.dir
+      size: 13232732
       nfiles: 2
   build_model:
     cmd: python build_model.py
     deps:
     - path: build_model.py
       hash: md5
-      md5: 662cd6b1562fbbc2c7d30dd0f2375a66
-      size: 3948
+      md5: 46bcc34f20c6851cd987640889eefde6
+      size: 3671
     - path: data/prepared_data
       hash: md5
-      md5: 5cbabd20ff23b9d6734c5c68684dc8dc.dir
-      size: 11982694
+      md5: ea0a2baf3931e692d6344ba609331089.dir
+      size: 13232732
       nfiles: 2
     params:
       configs/build_model.yaml:
@@ -48,7 +48,7 @@ stages:
     outs:
     - path: data/model/
       hash: md5
-      md5: f53ceced818ffe9e3ae327492d5a049a.dir
+      md5: eb2b910dec66481e75bb6058622f6e55.dir
       size: 1832
       nfiles: 1
   generate_predictions:
@@ -56,18 +56,18 @@ stages:
     deps:
     - path: data/model
       hash: md5
-      md5: f53ceced818ffe9e3ae327492d5a049a.dir
+      md5: eb2b910dec66481e75bb6058622f6e55.dir
       size: 1832
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 5cbabd20ff23b9d6734c5c68684dc8dc.dir
-      size: 11982694
+      md5: ea0a2baf3931e692d6344ba609331089.dir
+      size: 13232732
       nfiles: 2
     - path: generate_predictions.py
       hash: md5
-      md5: 32c0ecd082e1f8fc4426338d6629979c
-      size: 4686
+      md5: d412c8c9b48b59a29f569633280a6e7f
+      size: 4237
     params:
       configs/generate_predictions.yaml:
         input_dataclient_type: local
@@ -78,26 +78,26 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: e71d1d864228b3f3994217bfcdbcc5b7.dir
-      size: 643090
+      md5: 85ec3fa0cb387a7775eccd23185f7966.dir
+      size: 643406
       nfiles: 1
   generate_metrics:
     cmd: python generate_metrics.py
     deps:
     - path: data/predictions
       hash: md5
-      md5: e71d1d864228b3f3994217bfcdbcc5b7.dir
-      size: 643090
+      md5: 85ec3fa0cb387a7775eccd23185f7966.dir
+      size: 643406
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 5cbabd20ff23b9d6734c5c68684dc8dc.dir
-      size: 11982694
+      md5: ea0a2baf3931e692d6344ba609331089.dir
+      size: 13232732
       nfiles: 2
     - path: generate_metrics.py
       hash: md5
-      md5: 4709c42d93f8e717a3d9e4958e46cd76
-      size: 4587
+      md5: 5577a28107458dc1e6bcaaa098388095
+      size: 4144
     params:
       configs/generate_metrics.yaml:
         dataclient_type: local
@@ -108,8 +108,8 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: 915100dc1b46b4517a3e1d71d211849d
-      size: 179
+      md5: d79f798a272e6b50597be4d08ae48fa8
+      size: 180
   startup_cleanup:
     cmd: python startup_cleanup.py
     deps:
diff --git a/modules/ml-pipeline/src/pipeline/src/generate_metrics.py b/modules/ml-pipeline/src/pipeline/src/generate_metrics.py
index 3a5c668..7efeda9 100644
--- a/modules/ml-pipeline/src/pipeline/src/generate_metrics.py
+++ b/modules/ml-pipeline/src/pipeline/src/generate_metrics.py
@@ -5,17 +5,14 @@ After the model is built, we can evaluate its performance
 
 import os
 import yaml
-import json
 import pandas as pd
 from pathlib import Path
 from core.interface.InterfaceModels import MLModel
 from core.interface.InterfaceMetrics import MLMetrics
 from core.interface.InterfaceDataClient import DataClient
-from core.interface.InterfaceDataHandler import DataHandler
 from core.DataClient import dataclient_factory
 from core.MLModels import model_factory
 from core.MLMetrics import metrics_factory
-from core.DataHandler import datahandler_factory
 from core.Logger import logger
 
 
@@ -43,9 +40,8 @@ feature_process_params = yaml.safe_load(open(feature_process_path))
 
 
 def generate_metrics(
-    dataclient: DataClient,
-    input_datahandler: DataHandler,
-    output_datahandler: DataHandler,
+    input_dataclient: DataClient,
+    output_dataclient: DataClient,
     model: MLModel,
     metrics: MLMetrics,
     target: str,
@@ -62,17 +58,15 @@ def generate_metrics(
     logger.info("--- Loading test data ---")
     logger.info("-------------------------")
 
-    test_data = input_datahandler.load_data(
-        dataclient=dataclient, location=test_data_filepath
+    test_data = input_dataclient.load_data(
+        location=test_data_filepath,
     )
 
     logger.info("---------------------------")
     logger.info("--- Loading predictions ---")
     logger.info("---------------------------")
 
-    predictions = input_datahandler.load_data(
-        dataclient=dataclient, location=predictions_output_filepath
-    )
+    predictions = input_dataclient.load_data(location=predictions_output_filepath)
 
     logger.info("--------------------------")
     logger.info("--- Generating metrics ---")
@@ -87,9 +81,7 @@ def generate_metrics(
     logger.info("--- Saving metrics ---")
     logger.info("----------------------")
 
-    output_datahandler.save_data(
-        dataclient=dataclient, obj=metrics_output, location=metrics_output_filepath
-    )
+    output_dataclient.save_data(obj=metrics_output, location=metrics_output_filepath)
 
 
 if __name__ == "__main__":
@@ -100,23 +92,18 @@ if __name__ == "__main__":
 
     model = model_factory(build_model_params["model_type"])
 
+    # Use data client for input and output, as we use dvc to cache later to the cloud
     dataclient_type = generate_metrics_params["dataclient_type"]
-    dataclient = dataclient_factory(dataclient_type)
-    dataclient.ingest_configurations(client_params[dataclient_type])
-    dataclient.establish_client()
+    dataclient = dataclient_factory(
+        dataclient_type=dataclient_type,
+        dataclient_config=client_params[dataclient_type],
+    )
 
-    input_datahandler = datahandler_factory(
-        generate_metrics_params["input_datahandler_type"]
-    )
-    output_datahandler = datahandler_factory(
-        generate_metrics_params["output_datahandler_type"]
-    )
     metrics = metrics_factory(generate_metrics_params["metrics_type"])
 
     generate_metrics(
-        dataclient=dataclient,
-        input_datahandler=input_datahandler,
-        output_datahandler=output_datahandler,
+        input_dataclient=dataclient,
+        output_dataclient=dataclient,
         model=model,
         metrics=metrics,
         target=feature_process_params["feature_processor_config"]["target"],
diff --git a/modules/ml-pipeline/src/pipeline/src/generate_predictions.py b/modules/ml-pipeline/src/pipeline/src/generate_predictions.py
index 48e192b..f80ec18 100644
--- a/modules/ml-pipeline/src/pipeline/src/generate_predictions.py
+++ b/modules/ml-pipeline/src/pipeline/src/generate_predictions.py
@@ -5,15 +5,12 @@ After the model is built, we can evaluate its performance
 
 import os
 import yaml
-import json
 import pandas as pd
 from pathlib import Path
 from core.interface.InterfaceModels import MLModel
 from core.interface.InterfaceDataClient import DataClient
-from core.interface.InterfaceDataHandler import DataHandler
 from core.DataClient import dataclient_factory
 from core.MLModels import model_factory
-from core.DataHandler import datahandler_factory
 from core.Logger import logger
 
 
@@ -40,7 +37,6 @@ feature_process_params = yaml.safe_load(open(feature_process_path))
 def generate_predictions(
     input_dataclient: DataClient,
     output_dataclient: DataClient,
-    datahandler: DataHandler,
     model: MLModel,
     target: str,
     model_filepath: str,
@@ -56,9 +52,7 @@ def generate_predictions(
     logger.info("--- Loading test data ---")
     logger.info("-------------------------")
 
-    test_data = datahandler.load_data(
-        dataclient=input_dataclient, location=test_data_filepath
-    )
+    test_data = input_dataclient.load_data(location=test_data_filepath)
 
     logger.info("---------------------")
     logger.info("--- Loading model ---")
@@ -83,10 +77,8 @@ def generate_predictions(
     predictions_df = pd.DataFrame(predictions)
     predictions_df.columns = [predictions_column_name]
 
-    datahandler.save_data(
-        dataclient=output_dataclient,
-        obj=predictions_df,
-        location=predictions_output_filepath,
+    output_dataclient.save_data(
+        obj=predictions_df, location=predictions_output_filepath
     )
 
 
@@ -102,23 +94,20 @@ if __name__ == "__main__":
     # For predictions, we will want a cloud data client
 
     input_dataclient_type = generate_predictions_params["input_dataclient_type"]
-    input_dataclient = dataclient_factory(input_dataclient_type)
-    input_dataclient.ingest_configurations(config=client_params[input_dataclient_type])
-    input_dataclient.establish_client()
+    input_dataclient = dataclient_factory(
+        dataclient_type=input_dataclient_type,
+        dataclient_config=client_params[input_dataclient_type],
+    )
 
     output_dataclient_type = generate_predictions_params["output_dataclient_type"]
-    output_dataclient = dataclient_factory(output_dataclient_type)
-    output_dataclient.ingest_configurations(
-        config=client_params[output_dataclient_type]
+    output_dataclient = dataclient_factory(
+        dataclient_type=output_dataclient_type,
+        dataclient_config=client_params[output_dataclient_type],
     )
-    output_dataclient.establish_client()
-
-    datahandler = datahandler_factory(prepare_data_params["datahandler_type"])
 
     generate_predictions(
         input_dataclient=input_dataclient,
         output_dataclient=output_dataclient,
-        datahandler=datahandler,
         model=model,
         target=feature_process_params["feature_processor_config"]["target"],
         model_filepath=build_model_params["model_save_filepath"],
diff --git a/modules/ml-pipeline/src/pipeline/src/prepare_data.py b/modules/ml-pipeline/src/pipeline/src/prepare_data.py
index 7238513..851be48 100644
--- a/modules/ml-pipeline/src/pipeline/src/prepare_data.py
+++ b/modules/ml-pipeline/src/pipeline/src/prepare_data.py
@@ -50,11 +50,7 @@ def prepare_data(
     logger.info("--- Loading data ---")
     logger.info("--------------------")
 
-    data_filetype = Path(data_filepath).suffix
-
-    data = input_dataclient.load_data(
-        location=data_filepath, filetype=data_filetype, load_config={}
-    )
+    data = input_dataclient.load_data(location=data_filepath, load_config={})
 
     logger.info("--------------------------")
     logger.info("--- Feature Processing ---")
@@ -83,16 +79,10 @@ def prepare_data(
     logger.info("--- Outputting data ---")
     logger.info("-----------------------")
 
-    output_train_filetype = Path(output_train_filepath).suffix
-    output_dataclient.save_data(
-        obj=train, location=output_train_filepath, filetype=output_train_filetype
-    )
+    output_dataclient.save_data(obj=train, location=output_train_filepath)
 
     if test is not None:
-        output_test_filetype = Path(output_test_filepath).suffix
-        output_dataclient.save_data(
-            obj=test, location=output_test_filepath, filetype=output_test_filetype
-        )
+        output_dataclient.save_data(obj=test, location=output_test_filepath)
 
     return train, test