update

Signed-off-by: hao-xu5 <hxu44@apple.com>
feast-dev · HaoXuAI · Nov 6, 2025 · Oct 5, 2025 · Oct 5, 2025 · Oct 5, 2025
commit 931fdbdd4c1b4863b0bb03a58e18478a269cf460
@@ -4,13 +4,17 @@
 
 Spark data sources are tables or files that can be loaded from some Spark store (e.g. Hive or in-memory). They can also be specified by a SQL query.
 
+**New in Feast:** SparkSource now supports advanced table formats including **Apache Iceberg**, **Delta Lake**, and **Apache Hudi**, enabling ACID transactions, time travel, and schema evolution capabilities.
+
 ## Disclaimer
 
 The Spark data source does not achieve full test coverage.
 Please do not assume complete stability.
 
 ## Examples
 
+### Basic Examples
+
 Using a table reference from SparkSession (for example, either in-memory or a Hive Metastore):
 
 ```python
@@ -51,8 +55,111 @@ my_spark_source = SparkSource(
 )
 ```
 
+### Table Format Support
+
+SparkSource now supports advanced table formats for modern data lakehouse architectures:
+
+#### Apache Iceberg
+
+```python
+from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import SparkSource
+from feast.table_format import IcebergFormat
+
+# Basic Iceberg configuration
+iceberg_format = IcebergFormat(
+    catalog="my_catalog",
+    namespace="my_database"
+)
+
+my_spark_source = SparkSource(
+    name="user_features",
+    path="my_catalog.my_database.user_table",
+    table_format=iceberg_format,
+    timestamp_field="event_timestamp"
+)
+```
+
+Time travel with Iceberg:
+
+```python
+# Read from a specific snapshot
+iceberg_format = IcebergFormat(
+    catalog="spark_catalog",
+    namespace="lakehouse"
+)
+iceberg_format.set_property("snapshot-id", "123456789")
+
+my_spark_source = SparkSource(
+    name="historical_features",
+    path="spark_catalog.lakehouse.features",
+    table_format=iceberg_format,
+    timestamp_field="event_timestamp"
+)
+```
+
+#### Delta Lake
+
+```python
+from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import SparkSource
+from feast.table_format import DeltaFormat
+
+# Basic Delta configuration
+delta_format = DeltaFormat()
+
+my_spark_source = SparkSource(
+    name="transaction_features",
+    path="s3://my-bucket/delta-tables/transactions",
+    table_format=delta_format,
+    timestamp_field="transaction_timestamp"
+)
+```
+
+Time travel with Delta:
+
+```python
+# Read from a specific version
+delta_format = DeltaFormat()
+delta_format.set_property("versionAsOf", "5")
+
+my_spark_source = SparkSource(
+    name="historical_transactions",
+    path="s3://my-bucket/delta-tables/transactions",
+    table_format=delta_format,
+    timestamp_field="transaction_timestamp"
+)
+```
+
+#### Apache Hudi
+
+```python
+from feast.infra.offline_stores.contrib.spark_offline_store.spark_source import SparkSource
+from feast.table_format import HudiFormat
+
+# Basic Hudi configuration
+hudi_format = HudiFormat(
+    table_type="COPY_ON_WRITE",  # or "MERGE_ON_READ"
+    record_key="user_id",
+    precombine_field="updated_at"
+)
+
+my_spark_source = SparkSource(
+    name="user_profiles",
+    path="s3://my-bucket/hudi-tables/user_profiles",
+    table_format=hudi_format,
+    timestamp_field="event_timestamp"
+)
+```
+
+## Configuration Options
+
 The full set of configuration options is available [here](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSource).
 
+### Table Format Options
+
+- **IcebergFormat**: See [Python API reference](https://rtd.feast.dev/en/master/#feast.table_format.IcebergFormat)
+- **DeltaFormat**: See [Python API reference](https://rtd.feast.dev/en/master/#feast.table_format.DeltaFormat)
+- **HudiFormat**: See [Python API reference](https://rtd.feast.dev/en/master/#feast.table_format.HudiFormat)
+
 ## Supported Types
 
 Spark data sources support all eight primitive types and their corresponding array types.