diff --git a/examples/event-api/README.md b/examples/event-api/README.md
new file mode 100644
index 000000000..67bb09049
--- /dev/null
+++ b/examples/event-api/README.md
@@ -0,0 +1,28 @@
+# Example: Embedded event-processing API
+
+This example shows the use of Kaskada for implementing real-time, low-latency event processing 
+as part of a service implemented in Python.
+
+## Running
+
+Ensure your environment is setup. Using a virtual environment is reccommended
+
+```sh
+pip install -r requirements.txt
+```
+
+Next, run the service   
+
+```sh
+python server.py
+```
+
+You should see logs similar to "Waiting for events...".
+
+Now, in a separate terminal/process, you can start sending JSON events to the server.
+You'll see aggregated feature values in the response body:
+
+```sh
+curl -H "Content-Type: application/json" localhost:8080 -d '{"user": "me"}'
+# > {"count": 1, "count_1m": 1}
+```
\ No newline at end of file
diff --git a/examples/event-api/server.py b/examples/event-api/server.py
new file mode 100755
index 000000000..7e1b68832
--- /dev/null
+++ b/examples/event-api/server.py
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+
+import json, asyncio, time, uuid, asyncio
+import kaskada as kd
+from aiohttp import web
+
+async def main():
+    kd.init_session()
+    
+    start = time.time()
+    requestmap = dict()
+
+    # Initialize event source with historical data
+    events = kd.sources.PyList(
+        rows = [{"ts": start, "user": "user_1", "request_id": "12345678-1234-5678-1234-567812345678"}],
+        time_column = "ts",
+        key_column = "user",
+        time_unit = "s"
+    )
+
+    # Compute features over events
+    output = (kd.record({
+        "response": kd.record({
+            "count": events.count(),
+            "count_1m": events.count(window=kd.windows.Since.minutely())
+        }),
+        "request_id": events.col("request_id"),
+        "ts": events.col("ts"),
+    }))
+
+    # Receive JSON messages in real-time
+    async def handle_http(req: web.Request) -> web.Response:
+        data = await req.json()
+
+        # Add the current time to the event 
+        data["ts"] = time.time()
+
+        # Create a future so the aggregated result can be returned in the API response
+        request_id = str(uuid.uuid4()) 
+        requestmap[request_id] = asyncio.Future()
+        data["request_id"] = request_id
+
+        # Send the event to Kaskada to be processed as a stream
+        print(f"Got data: {data}")
+        events.add_rows(data)
+
+        # Wait for the response to be completed by the Kaskada handler
+        print(f"Waiting for response")
+        resp = await requestmap[request_id]
+
+        # Return result as the response body
+        print(f"Sending response: {resp}")
+        return web.Response(text = json.dumps(resp))
+
+    # Setup the async web server
+    app = web.Application()
+    app.router.add_post('/', handle_http)
+    runner = web.AppRunner(app)
+    await runner.setup()
+    site = web.TCPSite(runner, 'localhost', 8080)
+    await site.start()
+    
+
+    # Handle each conversation as it occurs
+    print(f"Waiting for events...")
+    async for row in output.run(materialize=True).iter_rows_async():
+        try:
+            # Ignore historical rows
+            if row["ts"] <= start:
+                continue
+
+            print(f"Recieved from K*: {row}")
+
+            request_id = row["request_id"]
+            fut = requestmap.pop(request_id, None)
+            if fut == None:
+                print(f"Unrecognized request_id: {request_id}")
+                continue
+
+            fut.set_result(row["response"])
+
+        except Exception as e:
+            print(f"Failed to handle live event from Kaskada: {e}") 
+
+    # Wait for web server to terminate gracefully
+    await runner.cleanup()
+
+if __name__ == "__main__":
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(main())
+    loop.run_forever()