diff --git a/examples/README.md b/examples/README.md
index d1cb72d1..8dbb399f 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -16,6 +16,7 @@ The following files are under *loaders_101*
 | File | Description | instructions |
 | -----| ------------| -----|
 | loaders.ipynb | A notebook with some sample code for aperturedb | Also available to read at [Aperturedb documentation](https://docs.aperturedata.io/HowToGuides/Advanced/loaders)|
+| data_loader_hierarchy.py | A script explaining the data loader hierarchy and tying them together | ``python data_loader_hierarchy.py``|
 
 ## Example 2: Image classification using a pretrained model
 The following files are under *image_classification*
diff --git a/examples/loaders_101/data_loader_hierarchy.py b/examples/loaders_101/data_loader_hierarchy.py
new file mode 100644
index 00000000..ef0635f0
--- /dev/null
+++ b/examples/loaders_101/data_loader_hierarchy.py
@@ -0,0 +1,109 @@
+"""
+Data Loader Hierarchy and Examples
+
+ApertureDB python SDK uses `ParallelLoader` as the main mechanism to efficiently batch and load data in parallel.
+The `ParallelLoader` relies on the `Subscriptable` interface.
+Classes that inherit from `Subscriptable` can be passed to `ParallelLoader.ingest()`.
+
+The main data loaders provided by ApertureDB handle parsing CSVs and generating the appropriate ApertureDB queries.
+The hierarchy is as follows:
+
+Subscriptable
+ └── CSVParser
+      ├── EntityDataCSV (Loads entities and their properties)
+      ├── ImageDataCSV (Loads images as entities and uploads image blobs)
+      ├── VideoDataCSV (Loads videos)
+      ├── BlobDataCSV (Loads generic blobs)
+      ├── DescriptorDataCSV (Loads descriptors, e.g., embeddings)
+      ├── ConnectionDataCSV (Loads connections between entities)
+      ├── BBoxDataCSV (Loads bounding boxes)
+      └── PolygonDataCSV (Loads polygons)
+
+Other non-CSV data loaders (like PyTorchData, KaggleData) also inherit from `Subscriptable`.
+
+The following script demonstrates how to instantiate different loaders and tie them together using `ParallelLoader`.
+"""
+
+import os
+import base64
+import tempfile
+import pandas as pd
+from aperturedb.Connector import Connector
+from aperturedb.ParallelLoader import ParallelLoader
+from aperturedb.EntityDataCSV import EntityDataCSV
+from aperturedb.ImageDataCSV import ImageDataCSV
+from aperturedb.ConnectionDataCSV import ConnectionDataCSV
+
+
+def create_sample_csvs(base_dir):
+    # 1. Create a sample CSV for Entities (Persons)
+    df_persons = pd.DataFrame({
+        "EntityClass": ["Person", "Person"],
+        "name": ["Alice", "Bob"],
+        "age": [25, 30],
+        "constraint_name": ["Alice", "Bob"]
+    })
+    persons_csv = os.path.join(base_dir, "persons.csv")
+    df_persons.to_csv(persons_csv, index=False)
+
+    # 2. Create a sample CSV for Images
+    # Note: the paths should point to real images in a real scenario
+    dummy_image_path = os.path.join(base_dir, "dummy_image.png")
+    # 1x1 transparent PNG base64
+    tiny_png_b64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII="
+    with open(dummy_image_path, "wb") as f:
+        f.write(base64.b64decode(tiny_png_b64))
+
+    df_images = pd.DataFrame({
+        "filename": [dummy_image_path, dummy_image_path],
+        "image_id": ["img1", "img2"],
+        "source": ["camera1", "camera2"],
+        "constraint_image_id": ["img1", "img2"]
+    })
+    images_csv = os.path.join(base_dir, "images.csv")
+    df_images.to_csv(images_csv, index=False)
+
+    # 3. Create a sample CSV for Connections (Person -> Image)
+    df_connections = pd.DataFrame({
+        "ConnectionClass": ["HasImage", "HasImage"],
+        "Person@name": ["Alice", "Bob"],
+        "_Image@image_id": ["img1", "img2"],
+        "connection_property": ["owns", "likes"]
+    })
+    connections_csv = os.path.join(base_dir, "connections.csv")
+    df_connections.to_csv(connections_csv, index=False)
+
+    return persons_csv, images_csv, connections_csv
+
+
+def main():
+    # Connect to ApertureDB (Make sure your ApertureDB instance is running)
+    db = Connector()
+
+    # Initialize the ParallelLoader
+    # The ParallelLoader handles the actual ingestion of queries produced by the Data Loader objects
+    loader = ParallelLoader(db)
+
+    with tempfile.TemporaryDirectory() as temp_dir:
+        persons_csv, images_csv, connections_csv = create_sample_csvs(temp_dir)
+
+        # 1. Load Entities
+        print("Loading Entities...")
+        # Properties are derived directly from the CSV header names.
+        person_loader = EntityDataCSV(persons_csv)
+        loader.ingest(person_loader)
+
+        # 2. Load Images
+        print("Loading Images...")
+        image_loader = ImageDataCSV(images_csv)
+        loader.ingest(image_loader)
+
+        # 3. Load Connections
+        print("Loading Connections...")
+        connection_loader = ConnectionDataCSV(connections_csv)
+        loader.ingest(connection_loader)
+        print("Done loading data!")
+
+
+if __name__ == "__main__":
+    main()