Feast: use a Push Source to stream real-time feature updates into the online (and optionally offline) store

Steps

1. Define a `PushSource(name=..., batch_source=<BatchSource>)` in your feature repo — the `batch_source` is optional and only needed if you want historical/point-in-time retrieval or offline materialization for that data
2. Reference the PushSource as the `source` of a `FeatureView`, including the entity join key in the schema
3. Register it with `feast apply`
4. At write time, call `FeatureStore(...).push("push_source_name", dataframe, to=PushMode.ONLINE_AND_OFFLINE)` — the `to` parameter defaults to `PushMode.ONLINE` if omitted; other options are `PushMode.OFFLINE` and `PushMode.ONLINE_AND_OFFLINE`
5. For streaming pipelines (e.g., PySpark Structured Streaming), call `store.push(feature_view_name, pandas_df)` inside a `foreachBatch` writer on the streaming DataFrame
6. Alternatively push data to a deployed Feast Python feature server's push endpoint instead of calling the SDK in-process