docs: add section on using denormalized for generating realtime features

docs/SUMMARY.md

@@ -137,6 +137,7 @@
 * [\[Beta\] On demand feature view](reference/beta-on-demand-feature-view.md)
 * [\[Alpha\] Vector Database](reference/alpha-vector-database.md)
 * [\[Alpha\] Data quality monitoring](reference/dqm.md)
+* [\[Alpha\] Streaming feature computation with Denormalized](reference/denormalized.md)
 * [Feast CLI reference](reference/feast-cli-commands.md)
 * [Python API reference](http://rtd.feast.dev)
 * [Usage](reference/usage.md)

docs/reference/denormalized.md

@@ -0,0 +1,110 @@
+# Streaming feature computation with Denormalized
+
+Denormalized makes it easy to compute real-time features and write them directly to your Feast feature store. This guide will walk you through setting up a streaming pipeline that computes feature aggregations and pushes them to Feast in real-time.
+
+## Prerequisites
+
+- Python 3.8+
+- Kafka cluster (local or remote)
+
+For a full working demo, check out the [feast-example](https://github.com/probably-nothing-labs/feast-example) repo.
+
+## Quick Start
+
+1. First, create a new Python project or use our template:
+```bash
+mkdir my-feature-project
+cd my-feature-project
+python -m venv .venv
+source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows
+pip install denormalized[feast] feast
+```
+
+2. Set up your Feast feature repository:
+```bash
+feast init feature_repo
+```
+
+## Project Structure
+
+Your project should look something like this:
+```
+my-feature-project/
+├── feature_repo/
+│   ├── feature_store.yaml
+│   └── sensor_data.py        # Feature definitions
+├── stream_job.py             # Denormalized pipeline
+└── main.py                   # Pipeline runner
+```
+
+## Define Your Features
+
+In `feature_repo/sensor_data.py`, define your feature view and entity:
+
+```python
+from feast import Entity, FeatureView, PushSource, Field
+from feast.types import Float64, String
+
+# Define your entity
+sensor = Entity(
+    name="sensor",
+    join_keys=["sensor_name"],
+)
+
+# Create a push source for real-time features
+source = PushSource(
+    name="push_sensor_statistics",
+    batch_source=your_batch_source  # Define your batch source
+)
+
+# Define your feature view
+stats_view = FeatureView(
+    name="sensor_statistics",
+    entities=[sensor],
+    schema=ds.get_feast_schema(),  # Denormalized handles this for you!
+    source=source,
+    online=True,
+)
+```
+
+## Create Your Streaming Pipeline
+
+In `stream_job.py`, define your streaming computations:
+
+```python
+from denormalized import Context, FeastDataStream
+from denormalized.datafusion import col, functions as f
+from feast import FeatureStore
+
+sample_event = {
+    "occurred_at_ms": 100,
+    "sensor_name": "foo",
+    "reading": 0.0,
+}
+
+# Create a stream from your Kafka topic
+ds = FeastDataStream(Context().from_topic("temperature", json.dumps(sample_event), "localhost:9092"))
+
+# Define your feature computations
+ds = ds.window(
+    [col("sensor_name")],  # Group by sensor
+    [
+        f.count(col("reading")).alias("count"),
+        f.min(col("reading")).alias("min"),
+        f.max(col("reading")).alias("max"),
+        f.avg(col("reading")).alias("average"),
+    ],
+    1000,  # Window size in ms
+    None   # Slide interval (None = tumbling window)
+)
+
+feature_store = FeatureStore(repo_path="feature_repo/")
+
+# This single line connects Denormalized to Feast!
+ds.write_feast_feature(feature_store, "push_sensor_statistics")
+```
+
+## Need Help?
+
+- Email us at hello@denormalized.io
+- Check out more examples on our [GitHub](https://github.com/probably-nothing-labs/denormalized)