Merge branch 'master' into feat/databricks-uc-provider

Author: falloficaruss

docs/getting-started/architecture/model-inference.md

@@ -17,7 +17,7 @@ of model inference):
 
 *Note: online features can be sourced from batch, streaming, or request data sources.*
 
-These three approaches have different tradeoffs but, in general, have significant implementation differences. 
+These four approaches have different tradeoffs but, in general, have significant implementation differences.
 
 ## 1. Online Model Inference with Online Features
 Online model inference with online features is a powerful approach to serving data-driven machine learning applications.
@@ -78,7 +78,7 @@ if features.to_dict().get('user_data:model_predictions') is None:
     model_predictions = model_server.predict(features)
     store.write_to_online_store(feature_view_name="user_data", df=pd.DataFrame(model_predictions))
 ```
-Note that in this case a seperate call to `write_to_online_store` is required when the underlying data changes and 
+Note that in this case a separate call to `write_to_online_store` is required when the underlying data changes and
 predictions change along with it.
 
 ```python

docs/getting-started/components/compute-engine.md

@@ -8,7 +8,7 @@ functions (UDFs).
 
 A materialization task abstracts over specific technologies or frameworks that are used to materialize data. It allows
 users to use a pure local serialized approach (which is the default LocalComputeEngine), or delegates the
-materialization to seperate components (e.g. AWS Lambda, as implemented by the the LambdaComputeEngine).
+materialization to separate components (e.g. AWS Lambda, as implemented by the LambdaComputeEngine).
 
 If the built-in engines are not sufficient, you can create your own custom materialization engine. Please
 see [this guide](../../how-to-guides/customizing-feast/creating-a-custom-compute-engine.md) for more details.

docs/getting-started/concepts/feast-types.md

@@ -8,6 +8,7 @@ Feast's type system is built on top of [protobuf](https://github.com/protocolbuf
 Feast supports the following categories of data types:
 
 - **Primitive types**: numerical values (`Int32`, `Int64`, `Float32`, `Float64`), `String`, `Bytes`, `Bool`, and `UnixTimestamp`.
+- **Zoned timestamp type**: `ZonedTimestamp` stores a timezone-aware datetime as both the UTC instant and its originating zone, so the original wall-clock zone round-trips losslessly. This differs from `UnixTimestamp`, which is always decoded as UTC and discards the source zone. Use `ZonedTimestamp` when local time-of-day or the offset/zone itself is meaningful. It must be explicitly declared in schema (it is not inferred by any backend), and is not supported as an entity key.
 - **Domain-specific primitives**: `PdfBytes` (PDF binary data for RAG/document pipelines) and `ImageBytes` (image binary data for multimodal pipelines). These are semantic aliases over `Bytes` and must be explicitly declared in schema — no backend infers them.
 - **UUID types**: `Uuid` and `TimeUuid` for universally unique identifiers. Stored as strings at the proto level but deserialized to `uuid.UUID` objects in Python.
 - **Array types**: ordered lists of any primitive type, e.g. `Array(Int64)`, `Array(String)`, `Array(Uuid)`.

docs/getting-started/concepts/feature-view.md

@@ -91,7 +91,7 @@ If the `schema` parameter is not specified in the creation of the feature view,
 
 "Entity aliases" can be specified to join `entity_dataframe` columns that do not match the column names in the source table of a FeatureView.
 
-This could be used if a user has no control over these column names or if there are multiple entities are a subclass of a more general entity. For example, "spammer" and "reporter" could be aliases of a "user" entity, and "origin" and "destination" could be aliases of a "location" entity as shown below.
+This could be used if a user has no control over these column names or if multiple entities are subclasses of a more general entity. For example, "spammer" and "reporter" could be aliases of a "user" entity, and "origin" and "destination" could be aliases of a "location" entity as shown below.
 
 It is suggested that you dynamically specify the new FeatureView name using `.with_name` and `join_key_map` override using `.with_join_key_map` instead of needing to register each new copy.
 
@@ -322,4 +322,4 @@ def driver_hourly_stats_stream(df: DataFrame):
     )
 ```
 
-See [here](https://github.com/feast-dev/streaming-tutorial) for a example of how to use stream feature views to register your own streaming data pipelines in Feast.
+See [here](https://github.com/feast-dev/streaming-tutorial) for an example of how to use stream feature views to register your own streaming data pipelines in Feast.

docs/getting-started/quickstart.md

@@ -4,7 +4,7 @@
 
 Feast (Feature Store) is an open-source feature store designed to facilitate the management and serving of machine learning features in a way that supports both batch and real-time applications.
 
-* *For Data Scientists*: Feast is a a tool where you can easily define, store, and retrieve your features for both model development and model deployment. By using Feast, you can focus on what you do best: build features that power your AI/ML models and maximize the value of your data.
+* *For Data Scientists*: Feast is a tool where you can easily define, store, and retrieve your features for both model development and model deployment. By using Feast, you can focus on what you do best: build features that power your AI/ML models and maximize the value of your data.
 
 * *For MLOps Engineers*: Feast is a library that allows you to connect your existing infrastructure (e.g., online database, application server, microservice, analytical database, and orchestration tooling) that enables your Data Scientists to ship features for their models to production using a friendly SDK without having to be concerned with software engineering challenges that occur from serving real-time production systems. By using Feast, you can focus on maintaining a resilient system, instead of implementing features for Data Scientists.
 

docs/reference/data-sources/kafka.md

@@ -72,4 +72,4 @@ def driver_hourly_stats_stream(df: DataFrame):
 ```
 
 ### Ingesting data
-See [here](https://github.com/feast-dev/streaming-tutorial) for a example of how to ingest data from a Kafka source into Feast.
+See [here](https://github.com/feast-dev/streaming-tutorial) for an example of how to ingest data from a Kafka source into Feast.

docs/reference/data-sources/kinesis.md

@@ -71,4 +71,4 @@ def driver_hourly_stats_stream(df: DataFrame):
 ```
 
 ### Ingesting data
-See [here](https://github.com/feast-dev/streaming-tutorial) for a example of how to ingest data from a Kafka source into Feast. The approach used in the tutorial can be easily adapted to work for Kinesis as well.
+See [here](https://github.com/feast-dev/streaming-tutorial) for an example of how to ingest data from a Kafka source into Feast. The approach used in the tutorial can be easily adapted to work for Kinesis as well.

docs/reference/online-stores/dynamodb.md

@@ -24,7 +24,7 @@ The full set of configuration options is available in [DynamoDBOnlineStoreConfig
 
 ## Configuration
 
-Below is a example with performance tuning options:
+Below is an example with performance tuning options:
 
 {% code title="feature_store.yaml" %}
 ```yaml

docs/reference/type-system.md

@@ -24,6 +24,7 @@ Feast supports the following data types:
 | `Bytes` | `bytes` | Binary data |
 | `Bool` | `bool` | Boolean value |
 | `UnixTimestamp` | `datetime` | Unix timestamp (nullable) |
+| `ZonedTimestamp` | `datetime` | Timezone-aware datetime preserving its source zone (nullable) |
 | `Uuid` | `uuid.UUID` | UUID (any version) |
 | `TimeUuid` | `uuid.UUID` | Time-based UUID (version 1) |
 | `Decimal` | `decimal.Decimal` | Arbitrary-precision decimal number |
@@ -202,7 +203,8 @@ from datetime import timedelta
 from feast import Entity, FeatureView, Field, FileSource
 from feast.types import (
     Int32, Int64, Float32, Float64, String, Bytes, Bool, UnixTimestamp,
-    Uuid, TimeUuid, Decimal, Array, Set, Map, ScalarMap, Json, Struct
+    Uuid, TimeUuid, Decimal, Array, Set, Map, ScalarMap, Json, Struct,
+    ZonedTimestamp
 )
 
 # Define a data source
@@ -232,6 +234,7 @@ user_features = FeatureView(
         Field(name="profile_picture", dtype=Bytes),
         Field(name="is_active", dtype=Bool),
         Field(name="last_login", dtype=UnixTimestamp),
+        Field(name="event_time", dtype=ZonedTimestamp),
         Field(name="session_id", dtype=Uuid),
         Field(name="event_id", dtype=TimeUuid),
         Field(name="price", dtype=Decimal),
@@ -362,6 +365,43 @@ unique_prices = {decimal.Decimal("9.99"), decimal.Decimal("19.99"), decimal.Deci
 `Decimal` is **not** inferred from any backend schema. You must declare it explicitly in your feature view schema. The pandas dtype for `Decimal` columns is `object` (holding `decimal.Decimal` instances), not a numeric dtype.
 {% endhint %}
 
+### ZonedTimestamp Type Usage Examples
+
+The `ZonedTimestamp` type stores a timezone-aware `datetime` as both the UTC instant
+and its originating zone, so the original wall-clock zone round-trips losslessly.
+By contrast, `UnixTimestamp` always decodes to UTC and discards the source zone.
+
+```python
+from datetime import datetime, timezone
+from zoneinfo import ZoneInfo
+
+# A datetime in a specific zone — both the instant and "America/Los_Angeles" are kept
+event_time = datetime(2026, 6, 17, 9, 0, 0, tzinfo=ZoneInfo("America/Los_Angeles"))
+
+# ZonedTimestamp values are returned as tz-aware datetime objects, in their own zone
+response = store.get_online_features(
+    features=["event_features:event_time"],
+    entity_rows=[{"user_id": 1001}],
+)
+result = response.to_dict()
+# result["event_time"][0] == event_time  (same instant AND same zone, e.g. 09:00-07:00)
+
+# Two values at the same instant but different zones stay distinct
+la = datetime(2026, 6, 17, 9, 0, 0, tzinfo=ZoneInfo("America/Los_Angeles"))
+utc = datetime(2026, 6, 17, 16, 0, 0, tzinfo=timezone.utc)  # same instant as `la`
+
+# A naive (tz-less) datetime is interpreted as UTC
+naive = datetime(2026, 6, 17, 12, 0, 0)  # stored zone is empty, decoded as UTC
+```
+
+{% hint style="warning" %}
+`ZonedTimestamp` is **not** inferred from any backend schema — you must declare it
+explicitly in your feature view schema. It is not supported as an entity key. The
+zone is stored as an IANA name (e.g. `America/Los_Angeles`) when available, falling
+back to a fixed-offset string; offline stores that cannot natively carry a zone may
+normalize to UTC on that backend.
+{% endhint %}
+
 ### Nested Collection Type Usage Examples
 
 ```python

docs/tutorials/validating-historical-features.md

@@ -661,7 +661,7 @@ _ = job.to_df(validation_reference=validation_reference)
     02/02/2022 02:43:53 PM INFO: Validating data_asset_name None with expectation_suite_name default
 
 
-Validation successfully passed as no exception were raised.
+Validation successfully passed as no exceptions were raised.
 
 
 ### 5. Validating new historical retrieval